METHODS: Observing anti-urolithiathic activity via in vitro nucleation and aggregation assay using a spectrophotometer followed by microscopic observation. A total of 12 methanolic extracts were tested to determine the potential extracts in anti-urolithiasis activities. Cystone was used as a positive control.

RESULTS: The results manifested an inhibition of nucleation activity (0.11 ± 2.32% to 55.39 ± 1.01%) and an aggregation activity (4.34 ± 0.68% to 58.78 ± 1.81%) at 360 min of incubation time. The highest inhibition percentage in nucleation assay was obtained by the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic pseudo-stem extract (2D) which was 55.39 ± 1.01%at 60 min of incubation time compared to the cystone at 30.87 ± 0.74%. On the other hand,the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic bagasse extract (3D) had the highest inhibition percentage in the aggregation assay incubated at 360 min which was obtained at 58.78 ± 1.8%; 5.53% higher than the cystone (53.25%).The microscopic image showed a great reduction in the calcium oxalate (CaOx) crystals formation and the size of crystals in 2D and 3D extracts, respectively, as compared to negative control.

METHODS: This was a cross-sectional study involving IC attending a PCU. Caregiver burden and psychological manifestations were measured using previously translated and validated Zarit Burden Interview and DASS-21 questionnaires respectively. Two hundred forty-nine samples were selected for analysis.

RESULT: The mean ZBI score was 23.33 ± 13.7. About half of the population 118(47.4%) was found to experienced caregiver burden whereby majority have mild to moderate burden 90(36.1%). The most common psychological manifestation among IC is anxiety 74(29.7%) followed by depression 51(20.4%) and stress 46(18.5%). Multiple logistic regression demonstrated that women who are IC to patients with non-malignancy were less likely to experience caregiver burden. IC who were highly educated and spent more than 14 h per day caregiving were at least twice likely to experience caregiver burden. Finally, those with symptoms of depression and anxiety were three times more likely to suffer from caregiver burden.

Purpose: The purpose of this study was to evaluate mean macular and RNFL thickness in pregnant women with GDM in a teaching institution in Malaysia. We also analyzed the association of age, HbA1c level, duration of GDM, type of treatment, family history, previous history of GDM and spherical equivalent with the macular and RNFL thickness.

Patients and Methods: This was a prospective and cross-sectional study involving 78 pregnant women with GDM, 72 healthy pregnant and 70 healthy non-pregnant women. The study was conducted in Hospital Universiti Sains Malaysia from 2016 to 2018. Macular and RNFL thickness were measured during the third trimester using spectral-domain optical coherence tomography. Age, HbA1c level, duration of GDM, type of treatment, family history, previous history of GDM and spherical equivalent were analysed.

Results: The mean macular thickness was 236.08 (16.44) µm, 237.26 (22.42) µm and 240.66 (20.95) µm for GDM, healthy pregnant, and healthy non-pregnant women. The mean RNFL thickness was 97.27 (9.14) µm, 99.83 (12.44) µm and 97.97 (10.07) µm for GDM, healthy pregnant, and healthy non-pregnant women. There was no significant difference in the mean macular and RNFL thickness in pregnant women with GDM when compared to the control groups (p>0.05). Age, HbA1c, duration of diabetes, treatment received, history of GDM and spherical equivalent did not show significant association with mean macular and retinal thickness (p>0.05).

RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.

METHODS: We conducted a systematic review of articles on the factors influencing under-five childhood immunisation uptake in Africa. This was achieved by using various keywords and searching multiple databases (Medline, PubMed, CINAHL and Psychology & Behavioral Sciences Collection) dating back from inception to 2020.

RESULTS: Out of 18,708 recorded citations retrieved, 10,396 titles were filtered and 324 titles remained. These 324 abstracts were screened leading to 51 included studies. Statistically significant factors found to influence childhood immunisation uptake were classified into modifiable and non-modifiable factors and were further categorised into different groups based on relevance. The modifiable factors include obstetric factors, maternal knowledge, maternal attitude, self-efficacy and maternal outcome expectation, whereas non-modifiable factors were sociodemographic factors of parent and child, logistic and administration factors.

METHODS: A modified Students Motivation towards Science Learning (SMTSL) was used to assess the digital learning usage and learning motivation among 150 UKM and 147 SUMS medical students throughout Year 1 to 5.