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  1. Fulltext Prevalence and determinants of pre-hypertension among undergraduate preclinical medical students in central Malaysia.
    Mohd Yusoff H, Mohd Tamil A, Mohd Fauzi MF, Mat Saruan NA
    IIUM Medical Journal Malaysia, 2019;18(3):65-72.
    MyJurnal
    Pre-hypertension, a classification of blood pressure ranging from 120–139 mm Hg systolic and/or 80–89 mm Hg diastolic, has been introduced to identify those high-risk group of developing hypertension to implement early intervention to halt disease progression. This study determines the prevalence of pre-hypertension and its determinants among undergraduate preclinical medical students in Malaysia.
    Materials & Methods: This was a cross-sectional study conducted among 158 registered second year medical students at one research university in central Malaysia from January to April 2018.
    Results: The prevalence of pre-hypertension was 20.6% among undergraduate preclinical medical students. The most respondent was female (69.9%), Malay ethnic (50.6%) who had normal body mass index (67.3%), no depression (79.5%), no anxiety (60.3%), no stress (68.6%), low physical activity level (44.9%), never smoked (95.5%), and never consumed alcohol (87.8%). Some respondents had positive family history of hypertension (43.6%) and diabetes mellitus (31.4%). After adjusted for all variables, gender (AOR=14.45, 95% CI 5.58-37.43) and depression status (AOR=6.37, 95% CI 1.29-31.49) were significantly associated with pre-hypertension.
    Conclusion: The prevalence of pre-hypertension among preclinical medical students was lower compared to other country, predicted by gender and depression status. However, further comprehensive multicentered studies in Malaysia with larger sample size is recommended to get more precise results in identifying determinants for pre-hypertension so that early intervention could be implemented nationwide.
  2. Computerized segmentation and measurement of chronic wound images
    Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN
    Comput Biol Med, 2015 May;60:74-85.
    PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015
    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
  3. Fulltext Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter
    Wan Ahmad WS, Zaki WM, Ahmad Fauzi MF
    Biomed Eng Online, 2015;14:20.
    PMID: 25889188 DOI: 10.1186/s12938-015-0014-8
    Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.
  4. Fulltext Why so stressed? A comparative study on stressors and stress between hospital and non-hospital nurses
    Muhamad Robat R, Mohd Fauzi MF, Mat Saruan NA, Mohd Yusoff H, Harith AA
    BMC Nurs, 2021 Jan 04;20(1):2.
    PMID: 33390159 DOI: 10.1186/s12912-020-00511-0
    BACKGROUND: Stress, which can be attributed to household and workplace stressors, is prevalent among nurses. However, these stressors' attribution may differ between hospital and non-hospital nurses. It is currently unknown whether there are significant differences in the sociodemographic and occupational characteristics between hospital and non-hospital nurses which may potentially influence the type and magnitude of stressors, and subsequently the stress status. Therefore, this study aims to estimate the prevalence of stress and compare the roles of sociodemograhic characteristics, occupational profiles, workplace stressors and household stressors in determining the stress status between hospital and non-hospital female nurses in Malaysia.

    METHODS: This cross-sectional study was conducted among randomly-selected 715 female nurses in Malaysia using pencil-and-paper self-reported questionnaires.

    RESULTS: The majority of participants were ever married (87.0%), having children (76.2%), and work in hospital setting (64.8%). The level of household stressors was generally similar between hospital and non-hospital nurses. However, hospital nurses significantly perceived higher level of workplace stressors. Shift work is significantly associated with higher level of household and workplace stressors among nurses in both groups. The level of stress was significantly higher among hospital nurses. Both household and workplace stressors explained about 40% of stress status in both hospital and non-hospital nurses.

    CONCLUSION: Hospital nurses are at higher risk of having stressors and stress as compared to non-hospital nurses, probably due to higher proportion of them involved in shift work. Hospital nurses should be given high priority in mitigating stress among nurses.

  5. Fulltext Fatigue and recovery among Malaysian doctors: the role of work-related activities during non-work time
    Mohd Fauzi MF, Mohd Yusoff H, Mat Saruan NA, Muhamad Robat R, Abdul Manaf MR, Ghazali M
    BMJ Open, 2020 09 25;10(9):e036849.
    PMID: 32978189 DOI: 10.1136/bmjopen-2020-036849
    OBJECTIVES: This paper aims to estimate the level of acute fatigue, chronic fatigue and intershift recovery among doctors working at public hospitals in Malaysia and determine their inter-relationship and their association with work-related activities during non-work time.

    DESIGN: Cross-sectional.

    SETTING: Seven core clinical disciplines from seven tertiary public hospitals in Malaysia.

    PARTICIPANTS: Study was conducted among 330 randomly-sampled doctors. Response rate was 80.61% (n=266).

    RESULTS: The mean score of acute fatigue, chronic fatigue and intershift recovery were 68.51 (SD=16.549), 54.60 (SD=21.259) and 37.29 (SD=19.540), respectively. All these scores were out of 100 points each. Acute and chronic fatigue were correlated (r=0.663), and both were negatively correlated with intershift recovery (r=-0.704 and r=-0.670, respectively). Among the work-related activities done during non-work time, work-related ruminations dominated both the more frequent activities and the association with poorer fatigue and recovery outcomes. Rumination on being scolded/violated was found to be positively associated with both acute fatigue (adjusted regression coefficient (Adj.b)=2.190, 95% CI=1.139 to 3.240) and chronic fatigue (Adj.b=5.089, 95% CI=3.876 to 6.303), and negatively associated with recovery (Adj.b=-3.316, 95% CI=-4.516 to -2.117). Doing work task at workplace or attending extra work-related activities such as locum and attending training were found to have negative associations with fatigue and positive associations with recovery. Nevertheless, doing work-related activities at home was positively associated with acute fatigue. In terms of communication, it was found that face-to-face conversation with partner did associate with higher recovery but virtual conversation with partner associated with higher acute fatigue and lower recovery.

    CONCLUSIONS: Work-related ruminations during non-work time were common and associated with poor fatigue and recovery outcomes while overt work activities done at workplace during non-work time were associated with better fatigue and recovery levels. There is a need for future studies with design that allow causal inference to address these relationships.

  6. Fulltext Maternal Mental Health following Ultrasonographic Detection of Fetal Structural Anomaly in the Midst of the COVID-19 Pandemic
    Roslan NR, Mohd Fauzi MF, Wan Teng L, Nur Azurah AG
    Int J Environ Res Public Health, 2021 12 07;18(24).
    PMID: 34948508 DOI: 10.3390/ijerph182412900
    Prenatal ultrasonographic detection of fetal structural anomaly may adversely affect maternal mental health throughout pregnancy, particularly in the current COVID-19 pandemic. This study aims to prospectively assess maternal stress, anxiety, and depression following ultrasonographic detection of fetal structural anomaly from diagnosis until delivery during the COVID-19 pandemic. A total of 141 pregnant women at a tertiary hospital who underwent detailed scans between 16 and 24 gestational weeks were included and categorized into the study (anomaly finding, n = 65) and comparison (normal finding, n = 76) groups. Self-administered questionnaires of 10-item Perceived Stress Scale (PSS-10) and Hospital Anxiety and Depression Scale (HADS) were used to assess maternal stress, anxiety, and depression at prior detection (T1), two-to-four weeks post-detection (T2), one-to-two weeks prior to delivery (T3), and one-to-two weeks post-delivery (T4). Repeated measures of analysis of variance (ANOVA) were conducted to assess time-, between-group, and time-group interaction effect. In general, maternal stress improved, but anxiety worsened, while depression persisted, over the time from T1 to T4. The average maternal stress and anxiety levels were significantly higher among groups with fetal anomaly. The maternal stress and anxiety level were significantly affected within one-to-two weeks post-detection of fetal structural anomaly. In conclusion, maternal mental health parameters were affected differently during the COVID-19 pandemic, with higher vulnerability of stress and anxiety among pregnant women with fetal structural anomaly particularly within one-to-two weeks post-detection.
  7. Fulltext A multi-resolution textural approach to diagnostic neuropathology reporting
    Fauzi MF, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, et al.
    J Neurooncol, 2015 Sep;124(3):393-402.
    PMID: 26255070 DOI: 10.1007/s11060-015-1872-4
    We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.
  8. Fulltext Pancreatic cancer grading in pathological images using deep learning convolutional neural networks
    Mohamad Sehmi MN, Ahmad Fauzi MF, Wan Ahmad WSHM, Wan Ling Chan E
    F1000Res, 2021;10:1057.
    PMID: 37767358 DOI: 10.12688/f1000research.73161.2
    Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.
  9. Fulltext Unplanned Absenteeism: The Role of Workplace and Non-Workplace Stressors
    Mat Saruan NA, Mohd Yusoff H, Mohd Fauzi MF, Wan Puteh SE, Muhamad Robat R
    Int J Environ Res Public Health, 2020 08 24;17(17).
    PMID: 32846878 DOI: 10.3390/ijerph17176132
    Unplanned absenteeism (UA), which includes medically certified leave (MC) or emergency leave (EL), among nurses may disturb the work performance of their team and disrupt the quality of patient care. Currently, there is limited study in Malaysia that examines the role of stressors in determining absenteeism among nurses. Therefore, apart from estimating the prevalence and the reasons of UA among nurses in Malaysia, this study aims to determine its stressor-related determinants. A cross-sectional study was conducted among 697 randomly sampled nurses working in Selangor, Malaysia. Most of them were female (97.3%), married (83.4%), and working in shifts (64.4%) in hospital settings (64.3%). In the past year, the prevalence of ever taking MC and EL were 49.1% and 48.4%, respectively. The mean frequency of MC and EL were 1.80 (SD = 1.593) and 1.92 (SD = 1.272) times, respectively. Meanwhile, the mean duration of MC and EL were 4.24 (SD = 10.355) and 2.39 (SD = 1.966) days, respectively. The most common reason for MC and EL was unspecified fever (39.2%) and child sickness (51.9%), respectively. The stressor-related determinants of durations of MC were inadequate preparation at the workplace (Adj.b = -1.065) and conflict with doctors (adjusted regression coefficient (Adj.b) = 0.491). On the other hand, the stressor-related determinants of durations of EL were conflict with spouse (Adj.b = 0.536), sexual conflict (Adj.b = -0.435), no babysitter (Adj.b = 0.440), inadequate preparation at workplace (Adj.b = 0.257), lack of staff support (Adj.b = -0.190) and conflict with doctors (Adj.b = -0.112). The stressor-related determinants of the frequency of MC were conflicts over household tasks (Adj.b = -0.261), no time with family (Adj.b = 0.257), dangerous surroundings (Adj.b = 0.734), conflict with close friends (Adj.b = -0.467), and death and dying (Adj.b = 0.051). In contrast, the stressor-related determinants of frequency of EL were not enough money (Adj.b = -0.334), conflicts with spouse (Adj.b = 0.383), pressure from relatives (Adj.b = 0.207), and inadequate preparation (Adj.b = 0.090). In conclusion, apart from the considerably high prevalence of unplanned absenteeism and its varying frequency, duration and reasons, there is no clear distinction in the role between workplace and non-workplace stressors in determining MC or EL among nurses in Malaysia; thus, preventive measures that target both type of stressors are warranted. Future studies should consider longitudinal design and mixed-method approaches using a comprehensive model of absenteeism.
  10. Fulltext Development and validation of Work-Related Activities during Non-Work Time Scale (WANTS) for doctors
    Mohd Fauzi MF, Mohd Yusoff H, Mat Saruan NA, Muhamad Robat R
    PLoS One, 2020;15(11):e0241577.
    PMID: 33206663 DOI: 10.1371/journal.pone.0241577
    Work-related activities during non-work time may influence the intershift recovery of post-work fatigue. Currently there is no valid and reliable scale available to measure the frequency for such activities among doctors. Therefore, this study aims to develop and validate 'Work-Related Activities during Non-Work Time Scale' (WANTS) that measure the frequency of work-related activities during non-work time for doctors. This was a scale development and validation study among doctors involving item generation, content and construct validation, and reliability assessment. 23-item seven-point Likert-type scale was developed through deductive (literature search) and inductive (interview with source population, authors' experiences, and expert opinion) methods. The content-validated scale was pre-tested, and the improved scale was subsequently administered to randomly-selected 460 doctors working at public hospital setting. Response rate was 77.76% (n = 382). Initial exploratory factor analysis (EFA) with principal axis factoring (PAF) using varimax rotation revealed unstable six-factor structure consisting of 17 variables; thus, we tested one- to six-factor model, and found that four-factor model is the most stable. Further analysis with principal component analysis (PCA) with a single component on each factor found that 17-variables four-factor model is stable. These factors were labelled as 'work-related thought', 'work-to-home conversation', 'task spillover' and 'superior-subordinate communication'. It showed good internal consistency with overall alpha value of 0.837. The scale is thus valid and reliable for measuring the frequency of each construct of work-related activities during non-work time among doctors.
  11. Fulltext Doctors' Mental Health in the Midst of COVID-19 Pandemic: The Roles of Work Demands and Recovery Experiences
    Mohd Fauzi MF, Mohd Yusoff H, Muhamad Robat R, Mat Saruan NA, Ismail KI, Mohd Haris AF
    Int J Environ Res Public Health, 2020 10 08;17(19).
    PMID: 33050004 DOI: 10.3390/ijerph17197340
    The COVID-19 pandemic potentially increases doctors' work demands and limits their recovery opportunity; this consequently puts them at a high risk of adverse mental health impacts. This study aims to estimate the level of doctors' fatigue, recovery, depression, anxiety, and stress, and exploring their association with work demands and recovery experiences. This was a cross-sectional study among all medical doctors working at all government health facilities in Selangor, Malaysia. Data were collected in May 2020 immediately following the COVID-19 contagion peak in Malaysia by using self-reported questionnaires through an online medium. The total participants were 1050 doctors. The majority of participants were non-resident non-specialist medical officers (55.7%) and work in the hospital setting (76.3%). The highest magnitude of work demands was mental demand (M = 7.54, SD = 1.998) while the lowest magnitude of recovery experiences was detachment (M = 9.22, SD = 5.043). Participants reported a higher acute fatigue level (M = 63.33, SD = 19.025) than chronic fatigue (M = 49.37, SD = 24.473) and intershift recovery (M = 49.97, SD = 19.480). The majority of them had no depression (69.0%), no anxiety (70.3%), and no stress (76.5%). Higher work demands and lower recovery experiences were generally associated with adverse mental health. For instance, emotional demands were positively associated with acute fatigue (adj. b = 2.73), chronic fatigue (adj. b = 3.64), depression (adj. b = 0.57), anxiety (adj. b = 0.47), and stress (adj. b = 0.64), while relaxation experiences were negatively associated with acute fatigue (adj. b = -0.53), chronic fatigue (adj. b = -0.53), depression (adj. b = -0.14), anxiety (adj. b = -0.11), and stress (adj. b = -0.15). However, higher detachment experience was associated with multiple mental health parameters in the opposite of the expected direction such as higher level of chronic fatigue (adj. b = 0.74), depression (adj. b = 0.15), anxiety (adj. b = 0.11), and stress (adj. b = 0.11), and lower level of intershift recovery (adj. b = -0.21). In conclusion, work demands generally worsen, while recovery experiences protect mental health during the COVID-19 pandemic with the caveat of the role of detachment experiences.
  12. Fulltext Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma
    Ahmad Fauzi MF, Wan Ahmad WSHM, Jamaluddin MF, Lee JTH, Khor SY, Looi LM, et al.
    Diagnostics (Basel), 2022 Dec 08;12(12).
    PMID: 36553102 DOI: 10.3390/diagnostics12123093
    Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific regions and classifies them into negatively, weakly, moderately, and strongly stained, followed by Allred scoring for ER status evaluation. The generated Allred score relies heavily on accurate cell detection and classification and is compared against pathologists' manual estimation. Experiments on 40 whole-slide images show 82.5% agreement on hormonal treatment recommendation, which we believe could be further improved with an advanced learning model and enhancement to address the cases with 0% ER status. This promising system can automate the exhaustive exercise to provide fast and reliable assistance to pathologists and medical personnel. The system has the potential to improve the overall standards of prognostic reporting for cancer patients, benefiting pathologists, patients, and also the public at large.
  13. Fulltext Deep-Learning-Based Approach in Cancer-Region Assessment from HER2-SISH Breast Histopathology Whole Slide Images
    Rehman ZU, Ahmad Fauzi MF, Wan Ahmad WSHM, Abas FS, Cheah PL, Chiew SF, et al.
    Cancers (Basel), 2024 Nov 11;16(22).
    PMID: 39594748 DOI: 10.3390/cancers16223794
    Fluorescence in situ hybridization (FISH) is widely regarded as the gold standard for evaluating human epidermal growth factor receptor 2 (HER2) status in breast cancer; however, it poses challenges such as the need for specialized training and issues related to signal degradation from dye quenching. Silver-enhanced in situ hybridization (SISH) serves as an automated alternative, employing permanent staining suitable for bright-field microscopy. Determining HER2 status involves distinguishing between "Amplified" and "Non-Amplified" regions by assessing HER2 and centromere 17 (CEN17) signals in SISH-stained slides. This study is the first to leverage deep learning for classifying Normal, Amplified, and Non-Amplified regions within HER2-SISH whole slide images (WSIs), which are notably more complex to analyze compared to hematoxylin and eosin (H&E)-stained slides. Our proposed approach consists of a two-stage process: first, we evaluate deep-learning models on annotated image regions, and then we apply the most effective model to WSIs for regional identification and localization. Subsequently, pseudo-color maps representing each class are overlaid, and the WSIs are reconstructed with these mapped regions. Using a private dataset of HER2-SISH breast cancer slides digitized at 40× magnification, we achieved a patch-level classification accuracy of 99.9% and a generalization accuracy of 78.8% by applying transfer learning with a Vision Transformer (ViT) model. The robustness of the model was further evaluated through k-fold cross-validation, yielding an average performance accuracy of 98%, with metrics reported alongside 95% confidence intervals to ensure statistical reliability. This method shows significant promise for clinical applications, particularly in assessing HER2 expression status in HER2-SISH histopathology images. It provides an automated solution that can aid pathologists in efficiently identifying HER2-amplified regions, thus enhancing diagnostic outcomes for breast cancer treatment.
  14. Fulltext Computational approach for counting of SISH amplification signals for HER2 status assessment
    Rehman ZU, Ahmad Fauzi MF, Wan Ahmad WSHM, Abas FS, Cheah PL, Chiew SF, et al.
    PeerJ Comput Sci, 2024;10:e2373.
    PMID: 39650490 DOI: 10.7717/peerj-cs.2373
    The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver in situ hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions. The Stardist deep learning model is fine-tuned on our HER2-SISH data to identify nuclei regions, followed by post-processing based on identified HER2 and CEP17 signals. Conventional thresholding techniques are used to segment HER2 and CEP17 signals. HER2 amplification status is determined by calculating the HER2-to-CEP17 signal ratio, in accordance with ASCO/CAP 2018 standards. The proposed method significantly reduces the effort and time required for quantification. Experimental results demonstrate a 0.91% correlation coefficient between pathologists manual enumeration and the proposed automatic SISH quantification approach. A one-sided paired t-test confirmed that the differences between the outcomes of the proposed method and the reference standard are statistically insignificant, with p-values exceeding 0.05. This study illustrates how deep learning can effectively automate HER2 status determination, demonstrating improvements over current manual methods and offering a robust, reproducible alternative for clinical practice.
  15. Fulltext Stress, Anxiety and Depression among a Cohort of Health Sciences Undergraduate Students: The Prevalence and Risk Factors
    Fauzi MF, Anuar TS, Teh LK, Lim WF, James RJ, Ahmad R, et al.
    Int J Environ Res Public Health, 2021 03 22;18(6).
    PMID: 33809939 DOI: 10.3390/ijerph18063269
    Stress, anxiety, and depression (SAD) have a negative impact on the learning and academic performance of university students. Hence, this study aimed to determine the prevalence, as well as the risk factors associated with SAD among a cohort of students pursuing undergraduate degree courses in health sciences. This is part of the strategy in building a healthy nation. A questionnaire containing socio-demographic factors and the short version of Depression, Anxiety, and Stress Scale-21 (DASS-21) was used to assess the likelihood of psychological distress. Logistic regression analysis was conducted to determine the risk factors of SAD. In total, 449 students completed the questionnaire (93.9% response rate). Of these, 65% had stress, 85.1% had anxiety and 51.4% had depression. Most cases of stress (74.6%) and depression (66.2%) were of normal-to-mild level, while 74.6% of them showed moderate-to-extremely severe anxiety. There was a statistically significant association between stress score and the year of study. In the regression analysis, poor sleep quality and fatigue were risk factors of anxiety and depression, whereas low-grade fever and frequent headaches were risk factors for stress and anxiety. Stress, anxiety, and depression scores were significantly higher among students studying medical imaging. A substantial proportion of health science students are suffering from SAD. This study recommends screening and close monitoring of the above-mentioned predictors and the formulation of comprehensive intervention strategies for students with SAD.
  16. Fulltext Classification of follicular lymphoma: the effect of computer aid on pathologists grading
    Fauzi MF, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, et al.
    BMC Med Inform Decis Mak, 2015;15:115.
    PMID: 26715518 DOI: 10.1186/s12911-015-0235-6
    Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.
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