• The effect of WIN55, 212-2 on protein S100, matrix metalloproteinase-2 and nitric oxide expression of chondrocyte monolayer

      Abdeldayum, Ali I.A.; Youseffi, Mansour; Sefat, Farshid; Genedy, Mohamed A.; Abdul Jamil, M.M.; Javid, F. (2017-01)
      Studies have been conducted to highlight the anti-inflammatory and immunosuppressive properties of synthetic cannabinoids as well as their potential for cartilage repair. Various wound healing techniques can be used to investigate the mechanisms of chondrocyte repair in monolayers or three dimensional tissues constructs. In this work the effect of WIN55, 212-2 (WIN-2) on nitric oxide (NO) and matrix metalloproteinase-2 (MMP-2) expressed by wounded chondrocyte monolayers was investigated. Moreover, expression of collagen type-I and type-II, fibronectin and S100 proteins were detected using immunofluorescence and quantitatively verified using ELISA based techniques following treatment with 1 μM and 2 μM of WIN-2. Treating chondrocytes with 1 μM of WIN-2 significantly increased expression of collagen type-II, fibronectin and S100, and significantly reduced collagen type-I expressions as compared to the control groups. On the other hand, both concentrations of WIN-2 significantly reduced the expression of the inflammation markers NO and MMP-2 in a dose dependent manner. These findings highlight the potential use of the synthetic cannabinoids for improving cartilage healing properties as well as acting as an anti-inflammatory agent which could be used to enhance tissue engineering protocols aimed at cartilage repair.
    • Electrocardiograph (ECG) circuit design and software-based processing using LabVIEW

      Abdul Jamil, M.M.; Soon, C.F.; Achilleos, A.; Youseffi, Mansour; Javid, F. (2017)
      The efficiency and acquisition of a clean (diagnosable) ECG signal dependent upon the proper selection of electronic components and the techniques used for noise elimination. Given that the human body and the lead cables act as antennas, hence picking up noises from the surroundings, thus a major part in the design of an ECG device is to apply various techniques for noise reduction at the early stage of the transmission and processing of the signal. This paper, therefore, covers the design and development of a Single Chanel 3-Lead Electrocardiograph and a Software-based processing environment. Main design characteristics include reduction of common mode voltages, good protection for the patient, use of the ECG device for both monitoring and automatic extraction (measurements) of the ECG components by the software. The hardware consisted of a lead selection stage for the user to select the bipolar lead for recording, a pre-amplification stage for amplifying the differential potentials while rejecting common mode voltages, an electrical isolation stage from three filtering stages with different bandwidths for noise attenuation, a power line interference reduction stage and a final amplification stage. A program in LabVIEW was developed to further improve the quality of the ECG signal, extract all its features and automatically calculate the main ECG output waveforms. The program had two main sections: The filtering section for removing power line interference, wideband noises and baseline wandering, and the analysis section for automatically extracting and measuring all the features of the ECG in real time. A Front Panel Environment was, therefore, developed for the user interface. The present system produced ECG tracings without the influence of noise/artefacts and provided accurate detection and measurement of all the components of the ECG signal.
    • A novel algorithm for human fall detection using height, velocity and position of the subject from depth maps

      Nizam, Y.; Abdul Jamil, M.M.; Mohd, M.N.H.; Youseffi, Mansour; Denyer, Morgan C.T. (2018-07)
      Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%.