Effectiveness of multivariate parameters on medical device disinfection using simultaneously-coupled triadic wavelength UV-LEDS

Ultraviolet light emitting diodes (UV-LEDs) have shown a great potential to replace traditional UV lamps for microorganisms disinfection. Most research is focused on water and food disinfection applications whereas the utilization of UVLEDs for healthcare disinfection is not fully understood due to limited exploration on this area. This study presented a comprehensive work on UV-LEDs solitary and coupled wavelength combinations in the context of inactivation, photoreactivation and morphological characteristics which is of significance to expand UV-LEDs scope beyond water applications, specifically to be adopted in healthcare disinfection system. In this study, UV-LEDs with peak emission at 276, 311 and 364 nm were studied for the inactivation of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Under the solitary wavelength, the effectiveness of each LED was studied separately and the effects of varied exposure times as well as UV doses were investigated for the inactivation of E. coli and S. aureus. It was found that the 276 nm LED produced the highest inactivation efficiency as compared to the 311 and 364 nm LEDs which required significantly lower exposure time and UV dose to achieve maximum inactivation of both bacteria. In the focus of investigating the effects of coupled wavelengths, simultaneously, the coupled triadic wavelength (SCTW) 276|311|364 nm UV-LEDs enhanced the inactivation effects and was able to produce the highest inactivation of E. coli (98.42%) and S. aureus (99.34%). The combination of 276|311 nm achieved the second best results on E. coli (97.36%) and S. aureus (98.63%), followed by the 276, 276|364, 311, 311|364 and 364 nm, respectively. For both, the solitary and the coupled wavelengths, a relatively higher inactivation of S. aureus was found in comparison with E. coli, indicating that S. aureus was more sensitive to UV irradiation. Moreover, the evaluation of morphological images of E. coli and S. aureus showed that all UV treated samples caused significant damage and deterioration of cellular membranes. However, the most pronounced damages such as membrane transparency, pore formation, blebs protrusion and lysis were seen when both bacteria were treated with the SCTW 276|311|364 nm UV-LEDs. The results also showed that the SCTW 276|311|364 nm UV-LEDs was the most effective combination in providing better persistence against the photoreactivation of E. coli (1.75%) and S. aureus (4.29%) as compared to the 276 nm which projected the photoreactivation of 2.14% on E. coli and 8.92% on S. aureus. These results are of significance for future applications in healthcare

K-means Clustering In Knee Cartilage Classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Effectiveness of visible and ultraviolet light emitting diodes for inactivation of Staphylococcus aureus, Pseudomonas aeruginosa,and Escherichia coli: a comparative study

The rapid use of ultraviolet light emitting diodes (UV-LEDs) in various disinfection applications is growing tremendously due to their advantages unachievable using UV lamps. In this study, a comparison of standard LED at 460 nm wavelength and UVA LED at 385 nm was conducted to determine their effectiveness in disinfection of frequently isolated pathogens in hospitals (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Determination of disinfection efficiency was carried out by measuring inhibition zone. Effects of varied exposure time on the inactivation of pathogenic microorganisms was studied. The results demonstrated that LED does not have germicidal activities. The highest inactivation for UVA LED was achieved for Pseudomonas aeruginosa. Linear relationship was found between exposure time and log reduction. This study showed that UVA LEDs can effectively inactivate significantly higher number of microorganisms hence can be used in disinfection of various applications

Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a lockedin state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain- Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements

Formulation of a novel HRV classification model as a surrogate fraudulence detection schema

Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%

Effectiveness of visible and ultraviolet light emitting diodes for inactivation of Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli: A comparative study

The rapid use of ultraviolet light emitting diodes (UV-LEDs) in various disinfection applications is growing tremendously due to their advantages unachievable using UV lamps. In this study, a comparison of standard LED at 460 nm wavelength and UVA LED at 385 nm was conducted to determine their effectiveness in disinfection of frequently isolated pathogens in hospitals (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Determination of disinfection efficiency was carried out by measuring inhibition zone. Effects of varied exposure time on the inactivation of pathogenic microorganisms was studied. The results demonstrated that LED does not have germicidal activities. The highest inactivation for UVA LED was achieved for Pseudomonas aeruginosa. Linear relationship was found between exposure time and log reduction. This study showed that UVA LEDs can effectively inactivate significantly higher number of microorganisms hence can be used in disinfection of various applications

Prominent region of interest contrast enhancement for knee MR images: data from the OAI

Osteoarthritis is the most commonly seen arthritis, where there are 30.8 million adults affected in 2015. Magnetic resonance imaging (MRI) plays a key role to provide direct visualization and quantitative measurement on knee cartilage to monitor the osteoarthritis progression. However, the visual quality of MRI data can be influenced by poor background luminance, complex human knee anatomy, and indistinctive tissue contrast. Typical histogram equalisation methods are proven to be irrelevant in processing the biomedical images due to their steep cumulative density function (CDF) mapping curve which could result in severe washout and distortion on subject details. In this paper, the prominent region of interest contrast enhancement method (PROICE) is proposed to separate the original histogram of a 16-bit biomedical image into two Gaussians that cover dark pixels region and bright pixels region respectively. After obtaining the mean of the brighter region, where our ROI – knee cartilage falls, the mean becomes a break point to process two Bezier transform curves separately. The Bezier curves are then combined to replace the typical CDF curve to equalize the original histogram. The enhanced image preserves knee feature as well as region of interest (ROI) mean brightness. The image enhancement performance tests show that PROICE has achieved the highest peak signal-to-noise ratio (PSNR=24.747±1.315dB), lowest absolute mean brightness error (AMBE=0.020±0.007) and notably structural similarity index (SSIM=0.935±0.019). In other words, PROICE has considerably outperformed the other approaches in terms of its noise reduction, perceived image quality, its precision and has shown great potential to visually assist physicians in their diagnosis and decision-making process

Incorporating Fuzzy Logic Into An Adaptive Negative Pressure Wound Therapy Device

Negative pressure wound therapy (NPWT) is a technique that enhances the healing process by applying negative pressure on the chronic or acute wounds. It has been diffusely adopted for treatment of trauma wound, chronic wound, or deep sternal wound infections due to its excellent healing result. However, there were several injuries and death cases caused by the unstable pressure generated from the wound treatment. This paper aims to design a stable negative pressure regulator by using fuzzy logic controller (FLC) .The proposed control approach is able to regulate the negative pressure within the desired range for healing process. The NPWT system consists of adhesive film dressing, wound dressing, fluid collecting canister, drainage tubes, vacuum pump, and microcontroller. The NPWT system developed is able to supply negative pressure from 0mmHg to 200mmHg and the negative pressure supply can be controlled. The effectiveness of FLC pressure controlling and Boolean logic controller method is validated by experiments. In conclusion, it is proven that the proposed method is able to provide a safe wound treatment in future

Bacterial disinfection and cell assessment post ultraviolet-C LED exposure for wound treatment

Ultraviolet-C sourced LED (UVC-LED) has been widely used for disinfection purposes due to its germicidal spectrum. In this study, the efficiencies of UVC-LED for Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) disinfections were investigated at three exposure distances (1, 1.5, and 2 cm) and two exposure times (30 and 60 s). The respective bacterial inhibition zones were measured, followed by a morphological analysis under SEM. The viabilities of human skin fibroblast cells were further evaluated under the treatment of UVC-LED with the adoption of aforesaid exposure parameters. The inhibition zones were increased with the increment of exposure distances and times. The highest records of 5.40 ± 0.10 cm P. aeruginosa inhibition and 5.43 ± 0.11 cm S. aureus inhibition were observed at the UVC-LED distance of 2 cm and 60-s exposure. Bacterial physical damage with debris formation and reduction in size were visualized following the UVC-LED exposures. The cell viability percentages were in a range of 75.20–99.00% and 82–100.00% for the 30- and 60-s exposures, respectively. Thus, UVC-LED with 275-nm wavelength is capable in providing bacterial disinfection while maintaining accountable cell viability which is suitable to be adopted in wound treatment.

K-means clustering in knee cartilage classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model