University of Malaya

About: University of Malaya is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Fiber laser. The organization has 25087 authors who have published 51491 publications receiving 1036791 citations. The organization is also known as: UM & Universiti Malaya.

Out‐of‐hospital Spinal Immobilization: Its Effect on Neurologic Injury

Abstract: Objective: To examine the effect of emergency immobilization on neurologic outcome of patients who have blunt traumatic spinal injuries. Methods: A 5-year retrospective chart review was carried out at 2 university hospitals. All patients with acute blunt traumatic spinal or spinal cord injuries transported directly from the injury site to the hospital were entered. None of the 120 patients seen at the University of Malaya had spinal immobilization during transport, whereas all 334 patients seen at the University of New Mexico did. The 2 hospitals were comparable in physician training and clinical resources. Neurologic injuries were assigned to 2 categories, disabling or not disabling, by 2 physicians acting independently and blinded to the hospital of origin. Data were analyzed using multivariate logistic regression, with hospital location, patient age, gender, anatomic level of injury, and injury mechanism serving as explanatory variables. Results: There was less neurologic disability in the unimmobilized Malaysian patients (OR 2.03; 95% CI 1.03–3.99; p = 0.04). This corresponds to a <2% chance that immobilization has any beneficial effect. Results were similar when the analysis was limited to patients with cervical injuries (OR 1.52; 95% CI 0.64–3.62; p = 0.34). Conclusion: Out-of-hospital immobilization has little or no effect on neurologic outcome in patients with blunt spinal injuries.

The Epstein-Barr virus and the pathogenesis of lymphoma

Abstract: Since the discovery in 1964 of the Epstein-Barr virus (EBV) in African Burkitt lymphoma, this virus has been associated with a remarkably diverse range of cancer types. Because EBV persists in the B cells of the asymptomatic host, it can easily be envisaged how it contributes to the development of B-cell lymphomas. However, EBV is also found in other cancers, including T-cell/natural killer cell lymphomas and several epithelial malignancies. Explaining the aetiological role of EBV is challenging, partly because the virus probably contributes differently to each tumour and partly because the available disease models cannot adequately recapitulate the subtle variations in the virus-host balance that exist between the different EBV-associated cancers. A further challenge is to identify the co-factors involved; because most persistently infected individuals will never develop an EBV-associated cancer, the virus cannot be working alone. This article will review what is known about the contribution of EBV to lymphoma development. Copyright (c) 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at √s=8 TeV

Abstract: Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb^(−1) for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z'_(SSM) resonance lighter than 2.90 TeV, a superstring-inspired Z'_ψ lighter than 2.57 TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Similarly, lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel.

Automated detection and localization of myocardial infarction using electrocardiogram

Abstract: Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy ( E a x ), signal energy (?x), fuzzy entropy ( E f x ), Kolmogorov-Sinai entropy ( E k s x ), permutation entropy ( E p x ), Renyi entropy ( E r x ), Shannon entropy ( E s h x ), Tsallis entropy ( E t s x ), wavelet entropy ( E w x ), fractal dimension ( F D x ), Kolmogorov complexity ( C k x ), and largest Lyapunov exponent ( E L L E x ) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.