Experimental Study of Pathological and Some Immunological Aspect of Infection Pseudomonas aeruginosa bacteria and Exotoxin in Rabbits

Pseudomonas aeruginosa is an opportunistic pathogen which infect immunocompromised patients. bacteria produce large types of virulence factors that serves its pathogenicity. The exotoxin A is major toxic extracellular virulent factor produced by P. aeruginosa. To clear the effect of exotoxin A and P. aeruginosa , Bacteria suspension and Exotoxin A extraction were injected intraperitonially in four group of rabbits, the result show there was significant decrease in total leukocyte count in all groups specially after 7 days from injection of Bacteria suspension and Exotoxin A also there is increase in neutrophilia percentage is the same period, the bacteria suspension and toxin A are capable alone or in both to activated phagocytosis, and produce neutralizing antibodies and produce pathological and immunological effect in liver spleen , kidney and lung and this suggest that toxin A and P. aeruginosa bacteria can effect in some immunological and pathological aspect when injected in experimental rabbit

Current Status Regarding Tumour Progression, Surveillance, Diagnosis, Staging, and Treatment Of HCC: A Literature Review

Hepatocellular carcinoma (HCC) is the most common primary malignant tumour of the liver, and is globally considered to be a major causes of cancer-associated mortality. The early diagnosis of HCC improves overall survival through the application of suitable treatment options. This article presents some of the techniques for the surveillance of HCC like ultrasonography and the use of tumour biomarkers such as α-fetoprotein (AFP), DesGamma-Carboxy Prothrombin (DCP) and others. Included in the discussion will be diagnostic methods like computed tomography (CT), magnetic resonance imaging (MRI), contrast enhancement ultrasound (CEUS), and fluorodeoxyglucose positron emission tomography hybrid with computed tomography (FDG PET/CT). Current molecular pathogenesis related to HCC and the molecular steps that determine the transition from benign to malignancy are also analysed. The HCC stages which depends on the Barcelona Clinic Liver Cancer (BCLC) algorithm are also discussed. Finally, this review article discusses the present therapeutic and treatment options for HCC such as resection, transplantation, or ablation used to treat early stage cancer. Also included will be trans-arterial chemoembolization (TACE) and Sorafenib for patients with intermediate and advanced-stage cancer, respectively

Laptop Riser, a Useful PBL Project for Diploma Students in Engineering Design

A useful project is identified for the semester-four diploma students in their final workshop of mechanical engineering program in the school of engineering at Australian college of Kuwait (ACK). ACK is putting significant emphasis in project based learning (PBL) and is developing new courses for both diploma and degree programs according to PBL style. In the final workshop project, it is required that the students design and manufacture a foldable laptop riser during fourteen weeks of their works. This project uses welding, cutting, drilling, and bending processes. It is expected that the deliverable product of this workshop is to be used in offices of ACK faculties and staff to raise the laptop height to provide an ergonomic and healthy office use. Students gain experiences in developing their own ideas, acquainted with preliminary design calculations, make sketches and drawings, build their laptop risers, and report their learning outcomes.  The students are allowed to work individually or in a team of two to three students. The students are asked to satisfy specific requirements and fulfill certain restrictions such as pre known available materials, sizes and dimensions, and quality of finished product. We found that students are satisfied with their learning and developed skills and also enjoyed to see their end products are utilized in the ACK offices

Lips tracking identification of a correct pronunciation of Quranic alphabets for tajweed teaching and learning

Mastering the recitation of the Holy Quran is an obligation among Muslims. It is an important task to fulfill other Ibadat like prayer, pilgrimage, and zikr. However, the traditional way of teaching Quran recitation is a hard task due to the extensive training time and effort required from both teacher and learner. In fact, learning the correct pronunciation of the Quranic letters or alphabets is the first step in mastering Tajweed (Rules and Guidance) in Quranic recitation. The pronunciation of Arabic alphabets is based on its points of articulation and the characteristics of a particular alphabet. In this paper, we implement a lip identification technique from video signal acquired from experts to extract the movement data of the lips while pronouncing the correct Quranic alphabets. The extracted lip movement data from experts helps in categorizing the alphabets into 5 groups and in deciding the final shape of the lips. Later, the technique was tested among a public reciter and then compared for similarity verification between the novice and the professional reciter. The system is able to extract the lip movement of the random user and draw the displacement graph and compare with the pronunciation of the expert. The error will be shown if the user has mistakenly pronounced the alphabet and suggests ways for improvement. More subjects with different backgrounds will be tested in the very near future with feedback instructions. Machine learning techniques will be implemented at a later stage for the real time learning application. Menguasai bacaan Al-Quran adalah satu kewajipan di kalangan umat Islam. Ia adalah satu tugas yang penting untuk memenuhi Ibadat lain seperti solat, haji, dan zikir. Walau bagaimanapun, cara tradisional pengajaran bacaan Al-Quran adalah satu tugas yang sukar kerana memerlukan masa latihan dan usaha yang banyak daripada guru dan pelajar. Malah, mempelajari sebutan yang betul bagi huruf Al-Quran adalah langkah pertama dalam menguasai Tajweed (Peraturan dan Panduan) pada bacaan Al-Quran. Sebutan huruf Arab adalah berdasarkan cara penyebutan tiap-tiap huruf dan ciri-ciri huruf tertentu. Dalam kertas ini, kami membina teknik pengenalan bibir dari isyarat video yang diperoleh daripada bacaan Al Quran oleh pakar-pakar untuk mengekstrak data pergerakan bibir ketika menyebut huruf Al-Quran yang betul. Data pergerakan bibir yang diekstrak daripada pembacaan oleh pakar membantu dalam mengkategorikan huruf kepada 5 kumpulan dan dalam menentukan bentuk akhir bibir. Kemudian, teknik ini diuji dengan pembaca awam dan kemudian bacaan mereka dibandingkan untuk pengesahan persamaan bacaan antara pembaca awam dan pembaca Al-Quran profesional. Sistem ini berjaya mengambil pergerakan bibir pengguna rawak dan melukis graf perbezaan sebutan mereka apabila dibandingkan dengan sebutan pakar. Jika pengguna telah tersilap menyebut sesuatu huruf, kesilapan akan ditunjukkan dan cara untuk penambahbaikan dicadangkan. Lebih ramai pengguna yang mempunyai latar belakang yang berbeza akan diuji dalam masa terdekat dan arahan maklum balas akan diberi. Teknik pembelajaran mesin akan dilaksanakan di peringkat seterusnya bagi penggunaan pembelajaran masa nyata

The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

The ironies of new innovation and the sunset industry: Diffusion and adoption

Agriculture plays a vital role in the Malaysian economy. Within the agriculture sector, paddy is considered important as it is the staple food for the nation. Innovation is considered as an important and necessary component in the development of agricultural activities, while communication is the powerful tool to further strengthen this sector. Technology adoption would only take place if innovation is driven by farmers’ need. Innovation diffusion technology transfer and adoption are all inter-related facets in increasing crop production. This study examined the influence of innovation attributes, communication channels and awareness in aiding diffusion and adoption of green fertilizer technology innovation in paddy farming in Malaysia. Past innovation diffusion studies have had limited emphasis on the importance of communication for diffusion and adoption of green fertilizer technologies. Hence, there exists a gap that demands specific studies to be undertaken. This study adopted a quantitative method through survey dissemination to fulfil the aim and objective of this study. 366 paddy farmers were selected from Perak to be the respondents. Demographic and multiple regression analysis were carried out on the data. Examining these results is an important first step toward understanding factors that could make the paddy sector in Malaysia more sustainable. From the analyzed data, this study found that certain attributes which are compatibility, interpersonal communication, mass media and awareness have an influence on green fertilizer technology adoption among the local farmers in Malaysia. The results indicate that the level of farmers awareness and information about innovation in general, innovation diffusion process and the extent of attributes in innovation. The study have several implications for government agencies and policy makers in the agricultural sector. Impact of study on Malaysian context are also proposed

Performance analysis of sigfox deployment

Low-power wide area network (LPWAN) has become a promising communication technology as alternative solutions on long range communications, low power consumption and low cost that overcome the problems faced by traditional communications. An example of LPWAN technology is Sigfox that uses a zero generation (0G) technology. However, there is not many literatures discussed on this technology especially on its applications. The objective of this paper is to study the coverage of Sigfox in Malaysia by tracing its network coverage using a Sigfox module that is integrated with an Arduino microprocessor. In this paper, we reported the experiments performed on real device and field test as well as observed the performance of the proposed technology. This system was tested using two different Sigfox development module which has device ID 4126D0 and 3E3D05. Two locations were selected to observe the connectivity of the Sigfox network. The results showed Sigfox deployment gave impact on a coastal location compared to urban area. Based on this finding, Sigfox is expected to have an improvised performance in the future especially applications in rural and coastal areas

An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals

With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques

Safety and Efficacy of Hydroxychloroquine in COVID-19: A Systematic Review and Meta-Analysis

Background: During the initial phases of the coronavirus disease 2019 (COVID-19) epidemic, there was an unfounded fervor surrounding the use of hydroxychloroquine (HCQ); however, recently, the Centers for Disease Control and Prevention (CDC) has recommended against routine use of HCQ outside of study protocols citing possible adverse outcomes. Methods: Multiple databases were searched to identify articles on COVID-19. An unadjusted odds ratio (OR) was used to calculate the safety and efficacy of HCQ on a random effect model. Results: Twelve studies comprising 3,912 patients (HCQ 2,512 and control 1400) were included. The odds of all-cause mortality (OR: 2.23, 95% confidence interval (CI): 1.58 - 3.13, P value \u3c 0.00001) were significantly higher in patients on HCQ compared to patients on control agent. The response to therapy assessed by negative repeat polymerase chain reaction (PCR) (OR: 1.83, 95% CI: 0.50 - 6.75, P = 0.36), radiological resolution (OR: 1.98, 95% CI: 0.47 - 8.36, P value = 0.36) and the need for invasive mechanical ventilation (IMV) (OR: 1.21, 95% CI: 0.34 - 4.33, P value = 0.76) were identical between the two groups. Overall, four times higher odds of net adverse events (NAEs) were observed in the HCQ group (OR: 4.59, 95% CI 1.73 - 12.20, P value = 0.02). The measures for individual safety endpoints were also numerically lower in the control arm; however, none of these values reached the level of statistical significance. Conclusions: HCQ might offer no benefits in terms of decreasing the viral load and radiological improvement in patients with COVID-19. HCQ appears to be associated with higher odds of all-cause mortality and NAEs

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios