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  1. Lim IR, Aw CY
    Med J Malaysia, 2003 Oct;58(4):613-6.
    PMID: 15190641
    Penetrating neck trauma present difficult management issues by virtue of their rarity. Undiagnosed laryngotracheal injuries have serious implications, especially in the context of multiple trauma, where other injuries overshadow that of the laryngotracheal complex. This is a case of a schizophrenic patient with multiple self-inflicted cuts on his throat and abdomen. Injuries include open, comminuted laryngeal complex lacerations with vocal cord avulsion, as well as evisceration of small bowel. Adequate assessment using both direct laryngoscopy and rigid endoscopy, coupled with open exploration, allowed optimal exposure and fixation of the larynx in the anatomical configuration. The post-operative outcome of the airway and voice remained satisfactory at follow-up. A high index of suspicion coupled with adequate surgical approach allowed establishment of a functional larynx.
  2. Lim IH, Alias R, Umapathy T, Samsudin A
    Med J Malaysia, 2019 Oct;74(5):433-435.
    PMID: 31649222
    Ocular chemical injury is a true ophthalmic emergency requiring immediate medical intervention. Damages can be devastating and potentially resulting in blindness, corneal perforation and phthisis bulbi. We describe here a successful treatment outcome in a patient who sustained Roper-Hall Grade 4 injury to both eyes. Patient received medical therapy followed by serial ocular surgeries with eventual visual recovery in one eye from counting finger to 6/15 after a decade. In conclusion, after maximum medical therapy, a carefully planned serial surgeries of cultivated oral mucosal epithelial transplantation (COMET) and PK has proven beneficial for this patient with advanced limbal stem cell deficiency (LSCD).
  3. Lim IL, Loo AVP, Subrayan V, Khang TF, See MH, Alip A, et al.
    Breast, 2018 Jun;39:117-122.
    PMID: 29660599 DOI: 10.1016/j.breast.2018.04.003
    It is now increasingly common for breast cancer patients to receive adjuvant tamoxifen therapy for a period of up to 10 years. As survival rate increases, managing tamoxifen ocular toxicities is important for patients' quality of life. Macular pigments in photoreceptor cells protect against free radical damage, which can cause macular degeneration. By reducing macular pigment concentration, tamoxifen may increase the risk of macular degeneration. Here, we compared macular pigment optical density (MPOD) and central macular thickness between breast cancer patients on tamoxifen adjuvant therapy (n = 70), and a control group (n = 72). Multiple regression analysis indicated that MPOD decreases with increasing tamoxifen dosage, up to a threshold of about 20 g, after which MPOD plateaus out. Mean MPOD in the treatment group (mean = 0.40) was significantly lower (p-value = 0.02) compared to the control group (mean = 0.47) for the left eye, and for the right eye (treatment mean = 0.39; control mean = 0.48; p-value = 0.009). No significant difference in mean central macular thickness was found between the treatment and the control group (p-values > 0.4). In the control group, MPOD and central macular thickness showed significant correlation (r∼0.30; p-values 
  4. Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, et al.
    Comput Biol Med, 2018 03 01;94:19-26.
    PMID: 29358103 DOI: 10.1016/j.compbiomed.2017.12.023
    Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.
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