Background Non-alcoholic fatty liver disease (NAFLD) is an emerging novel cardiovascular disease (CVD) risk factor. It’s prevalence is increasing globally. However, there is paucity in the evidence showing the association between NAFLD and CVD risk in primary care setting. Therefore, the objectives of this study were to determine the prevalence and factors associated with NAFLD among patients with ≥1 risk factor for NAFLD or CVD attending primary care clinics. Methodology A cross sectional study was conducted in two clinics at a university primary care centre. Patients aged ≥18 years with ≥1 risk factor for NAFLD or CVD were recruited. Participants with history of established liver disease or chronic alcohol use were excluded. Socio-demographics, clinical related data, anthropometric measurements and blood investigation results were recorded in a proforma. Diagnosis of NAFLD was made using abdominal ultrasound. The 10-year CVD risk was calculated using the general Framingham Risk Score (FRS). Multiple logistic regression (MLogR) was performed to identify independent factors associated with NAFLD. Results A total of 263 participants were recruited. The mean age was 52.3 ± 14.7 years old. Male and female were equally distributed. Majority of the participants were Malays (79.8%). The overall prevalence of NAFLD was 54.4% (95%CI 48,60%). Participants in the high FRS category have higher prevalence of NAFLD (65.5%), followed by those in the moderate category (55.4%) and the low category (46.3%), p = 0.025. From MLogR, independent factors associated with NAFLD were being employed (OR = 2.44, 95%CI 1.26,4.70, p = 0.008), obesity with BMI ≥27.5 (OR = 2.89, 95%CI 1.21,6.91, p = 0.017), elevated fasting glucose ≥5.6 mmol/L (OR = 2.79, 95%CI 1.44,5.43, p = 0.002), ALT ≥34 U/L (OR = 3.70, 95%CI 1.85,7.44, p < 0.001) and high FRS category (OR = 2.82, 95%CI 1.28,6.23, p = 0.010). Conclusion NAFLD is highly prevalent among patients with ≥1 risk factor for NAFLD or CVD in these primary care clinics. Patients who were obese, have elevated fasting glucose, elevated ALT and in the high FRS category were more likely to have NAFLD. This study underscores the importance of targeted screening for NAFLD in those with risk factors in primary care. Aggressive intervention must be executed in those with NAFLD in order to reduce CVD complications and risk of progression.

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Abstract. The population size, population density and rate of urbanization are often crediting to contributing increasing a crime pattern specially in city. Urbanism model stating that the rise in urban crime and social problems is based on three population indicators namely; size, density and heterogeneity. The objective of this paper is to identify crime patterns of the hot spot urban crime location and the factors influencing the crime pattern relationship with population size, population density and rate of urbanization population. This study employed the ArcGIS Pro 2.4 tool such as Emerging Hot Spot Analysis (Space Time) to determine a crime pattern and Ordinary Least Squares (OLS) Regression to determine the factors influencing the crime patterns. By using these analyses tools, this study found that 54 (53%) out of 102 total neighbourhood locations (2011–2017 years) had a 99 percent significance confidence level where z-score exceeded +2.58 with a small p-value (p < 0.01) as the hot spot crime location. The result of data analysis using OLS regression explains that combination of exploratory variable model rate of urbanization and population size contributes 56 percent (R2 = 0.559) variance in crime index rate incident [F (3,39) = 18.779, p < 0.01). While the population density (β = 0.045, t = 0.700, p > 0.10) is not a significance contributes to the change in crime index rate in Petaling and Klang district. The importance of the study is useful information for encouraging government and law enforcement agencies to promote safety and reduce risk of urban population crime areas.

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention.