METHODS: This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering.
RESULTS: A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts.
CONCLUSIONS: A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.
METHODS: This qualitative study employed the hermeneutic phenomenological study design. Purposive sampling strategies were used to recruit Malaysians that had direct experiences with friends, family members and their community who were hesitating or refusing to accept the COVID-19 vaccines. A semi-structured interview guide was developed based on the expert knowledge of the investigators and existing literature on the topic. A series of focus group interviews (FGIs) was conducted online facilitated by a multidisciplinary team of experts. The group interviews were transcribed verbatim and analysed.
RESULTS: Fifty-nine participants took part in seven FGIs. We found that "incongruence" was the overall thematic meaning that connected all the 3 main themes. These themes comprise firstly, the incongruence between the aims and implementation of the National Immunization Program which highlighted the gap between realities and needs on the ground. Secondly, the incongruence between Trust and Mistrust revealed a trust deficit in the government, COVID-19 news, and younger people's preference to follow the examples of local vaccination "heroes". Thirdly, the incongruence in communication showed the populace's mixed views regarding official media and local social media.
CONCLUSIONS: This study provided rich details on the complex picture of the COVID-19 immunization program in Malaysia and its impact on vaccine hesitancy and refusal. The inter-related and incongruent factors explained the operational difficulty and complexity of the NIP and the design of an effective health communication campaign. Identified gaps such as logistical implementation and communication strategies should be noted by policymakers in implementing mitigation plans.
METHOD: Forty-one T2DM patients on follow-up at a community clinic were divided into normo-(NA), micro-(MIC), and macroalbuminuria (MAC) groups. Differential levels of miRNAs in 12 samples were determined using the pathway-focused (human fibrosis) miScript miRNA qPCR array and was validated in 33 samples, using the miScript custom qPCR array (CMIHS02742) (Qiagen GmbH, Hilden, Germany).
RESULTS: Trends of upregulation of 3 miRNAs in the serum, namely, miR-874-3p, miR-101-3p, and miR-145-5p of T2DM patients with MAC compared to those with NA. Statistically significant upregulation of miR-874-3p (p = 0.04) and miR-101-3p (p = 0.01) was seen in validation cohort. Significant negative correlations between the estimated glomerular filtration rate (eGFR) and miR-874-3p (p = 0.05), miR-101-3p (p = 0.03), and miR-145-5p (p = 0.05) as well as positive correlation between miR-874-3p and age (p = 0.03) were shown by Pearson's correlation coefficient analysis.
CONCLUSION: Upregulation of previously known miRNA, namely, miR-145-5p, and possibly novel ones, namely, miR-874-3p and miR-101-3p in the serum of T2DM patients, was found in this study. There was a significant correlation between the eGFR and these miRNAs. The findings of this study have provided encouraging evidence to further investigate the putative roles of these differentially expressed miRNAs in DKD.
MATERIALS AND METHODS: Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score.
RESULTS: The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively.
CONCLUSION: Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance.
KEY POINTS: • Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.