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  1. B, Elamathi, R, Vijaya, V, Valliappan, A, Ramanathan
    Ann Dent, 2014;21(1):33-37.
    MyJurnal
    According to the 3rd edition of the international
    classification of headache disorders (ICHD3 2013),
    Trigeminal Neuralgia (TN) is classified into two types:
    1. Classical TN, purely paroxysmal 2. Classical TN
    with concomitant persistent facial pain. In this article,
    the authors describe a 47 year-old, male with unilateral,
    severe, recurring, electric shock-like pain involving left
    lower jaw, teeth and gingiva. Diagnosis of classical TN
    of the left 3rd division of the trigeminal nerve was made.
    The patient was treated with pharmacotherapeutic agents
    but without relief. Magnetic resonance imaging (MRI)
    of the brain showed medial vascular compression of left
    trigeminal pontine root entry zone caused by superior
    cerebellar artery. A microvascular decompression (MVD)
    surgery was done at the left trigeminal pontine root entry
    zone resulting in good relief of pain. This article highlights
    the differential diagnoses to be considered with TN and
    also emphasize the difference between the two types of
    the TN according to ICDH3 (2013). It also highlights the
    difference between classical TN purely paroxysmal with
    and without vascular compression by imaging techniques
    and their differing treatment modalities, which therefore
    should be reflected in future ICDH classification.
  2. A, Ramanathan, RM, Chong, ZW, Tay, C, Siow-Wee, TG, Kallarakkal, Kassim NLA
    Ann Dent, 2014;21(1):16-23.
    MyJurnal
    There is significant amount of research done
    on Oral Squamous cell carcinoma (OSCC). One research
    technique is immunohistochemical (IHC) analysis using
    whole sections. With little availability of OSCC tissues
    high throughput analysis such as Tissue Microarray
    (TMA) are capable of efficient analysis of small samples.
    However, the results become questionable if the tumor
    exhibits high degree of heterogeneity as TMA cores might
    not accurately represent the whole section. Aim: The aim
    of this study is to determine the optimal number of TMA
    cores required to provide an accurate representation of
    the whole section with IHC analysis in OSCC. Materials
    and Methods: Twenty tissue samples stained with
    anti-p53 antibody were scanned at 40x magnification.
    Three to six virtual cores of size 0.6 mm, 1.0 mm and
    1.5 mm were drawn on the scanned slides. H-scores
    were obtained for both whole sections and cores using
    NuclearQuant (3DHistech, Budapest, Hungary) software
    after eliminating non-tumour cells and artifacts manually.
    The correspondence between the cores and whole sections
    were calculated using intra-class correlation and one
    sample t-test. Results: Good correlation was obtained
    with just a single core of 0.6mm (0.826). Subsequent
    increase in core number and size resulted in improved
    correlation coefficient and smaller confidence interval.
    Conclusion: Three TMA cores of 0.6 mm would be the
    most optimal, as not only was there very strong correlation
    with the whole tissue section, the extra core will also be
    able to act as confirmation if the results of the first 2 cores
    are in doubt.
  3. A, Ramanathan, NA, Rosedee, SA, Edwer, EP, John, K, Palaniswany, Bakar ZA
    Ann Dent, 2014;21(1):6-15.
    MyJurnal
    The prevalence of oral mucosal lesions in elderly
    population is 22.8% to 61.6%. Conventional oral
    examination (COE) is usually carried out to detect oral
    mucosal lesions (OML). However, new diagnostic aids
    have been introduced to improve OML detection. This
    study aimed to determine the utility of autofluorescence
    (AF) imaging in detecting OML from normal oral mucosa
    and its anatomic variation among institutionalised elderly
    Malaysian when compared with COE. Fifty subjects
    randomly selected from 9 nursing homes and COE and AF
    imaging using VELscope Vx, (LED Dental, Vancouver,
    British Columbia, Canada) were carried out. Sensitivity,
    specificity, positive (PPV) and negative predictive value
    (NPV) and accuracy of AF imaging were calculated.
    From the study, fifteen subjects had normal oral mucosa,
    15 anatomic variations and 36 lesions were identified.
    The sensitivity and specificity of AF imaging were 100%
    and 70% whereas the PPV and NPV were 80% and 100%
    respectively. The accuracy of AF imaging was 86.37%
    when compared to COE. In conclusion AF imaging was
    able to detect OML and differentiate them from normal
    oral mucosa. However it has limited usefulness in
    differentiating between these lesions. This study however,
    was able to detail the AF imaging profile of normal oral
    mucosa, its normal variants and some common reactive or
    infective lesions which can be used in future OML studies
    as comparison to oral potentially malignant lesions.
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