Search Results (8)

Background: COVID-19 is an emerging global pandemic with potential adverse effects during pregnancy. This study aimed to determine the adverse maternal and foetal outcomes due to COVID-19 infection. We also compared maternal and neonatal outcomes with regard to the timing of diagnosis (first and second trimester vs. third and fourth trimester); early COVID-19 (stage I and II) vs. severe-stage COVID-19 (III, IV, and V); and lastly, women who were partially vaccinated vs. unvaccinated. Methods: This was a retrospective study conducted in HCTM from January 2021 to January 2022. All pregnant women admitted for COVID-19 infections were recruited. The patients’ records were traced. Adverse maternal and neonatal outcomes were documented and analysed. Results: There were 172 pregnant women recruited into this study. We excluded twenty-four patients with incomplete data and nine women who delivered elsewhere. The final 139 patients were available for data analysis. The majority of women were in their third trimester of pregnancy (87.8%); however, only 5.0% and 7.2% were in the first and second trimesters, respectively. The study population had a median BMI of 29.1 kg/m² and almost half of them had never received a COVID-19 vaccination. A sub-analysis of data concerning adverse maternal and foetal outcomes comparing early vs. severe stages of COVID-19 infection showed that severe-stage disease increased the risk of preterm birth (54.5% vs. 15.4%, p < 0.001) and preterm birth before 34 weeks (31.9% vs. 2.6%, p < 0.001) significantly. The severe-stage disease also increased NICU admission (40.9% vs. 15.4%, p = 0.017) with lower birth weight (2995 g vs. 2770 g, p = 0.017). The unvaccinated mothers had an increased risk of preterm birth before 34 weeks and this was statistically significant (11.6% vs. 2.9%, p = 0.048). Conclusions: Adverse pregnancy outcomes such as ICU admission or patient death could occur; however, the clinical course of COVID-19 in most women was not severe and the infection did not significantly influence the pregnancy. The risk of preterm birth before 34 weeks was higher in a more severe-stage disease and unvaccinated mother. The findings from this study can guide and enhance antenatal counselling of women with COVID-19 infection, although they should be interpreted with caution in view of the very small number of included cases of patients in the first and second trimesters. Full article

Cancer of the brain is most common in the elderly and young and can be fatal in both. Brain tumours can heal better if they are diagnosed and treated quickly. When it comes to processing medical images, the deep learning method is essential in aiding humans in diagnosing various diseases. Classifying brain tumours is an essential step that relies heavily on the doctor’s experience and training. A smart system for detecting and classifying these tumours is essential to aid in the non-invasive diagnosis of brain tumours using MRI (magnetic resonance imaging) images. This work presents a novel hybrid deep learning CNN-based structure to distinguish between three distinct types of human brain tumours through MRI scans. This paper proposes a method that employs a dual approach to classification using deep learning and CNN. The first approach combines the unsupervised classification of an SVM for pattern classification with a pre-trained CNN (i.e., SqueezeNet) for feature extraction. The second approach combines the supervised soft-max classifier with a finely tuned SqueezeNet. To evaluate the efficacy of the suggested method, MRI scans of the brain were used to analyse a total of 1937 images of glioma tumours, 926 images of meningioma tumours, 926 images of pituitary tumours, and 396 images of a normal brain. According to the experiment results, the finely tuned SqueezeNet model obtained an accuracy of 96.5%. However, when SqueezeNet was used as a feature extractor and an SVM classifier was applied, recognition accuracy increased to 98.7%. Full article

In Malaysia, the main constraints of rice yield and productivity are infertile soils and poor management practices because these soils are characterized by low pH, low nutrient availability, low organic matter, and high exchangeable Al and Fe ions, due to high rainfall and hot temperatures. Thus, an incubation study was conducted to determine the optimum amount of calcium silicate (HmbG brand) to improve the soil pH, electrical conductivity (EC), exchangeable Al, available P, and cation exchange capacity (CEC) of a paddy soil in Sabah, Malaysia. The Kelawat series (Typic Dystrudept) soil was incubated with calcium silicate at the application rates of 0 (T1), 1 (T2), 2 (T3), and 3 t ha⁻¹ (T4) using a Completely Randomized Design (CRD) in triplicates for 30, 60, 90, and 120 days. The calcium silicate used significantly improved soil pH because of the release of SiO₄⁴⁻ and Ca²⁺ ions, which neutralized and immobilized H⁺ ions. Furthermore, the neutralizing effects of the amendment impeded Al hydrolysis by up to 57.4% and this resulted in an increase in the available P in the soil by 31.26% to 50.64%. The increased availability of P in the soil was also due to the high affinity of SiO₄⁴⁻ to desorb P from soil minerals and it is believed that SiO₄⁴⁻ can temporarily adsorb exchangeable base cations such as K⁺, Ca²⁺, Mg²⁺, and Na⁺. Moreover, applying calcium silicate at 3 t ha⁻¹ improved soil CEC by up to 54.84% compared to that of untreated soils (T1) because of increased pH and the number of negatively charged sites. The most suitable application rate of the calcium silicate was found to be 3 t ha⁻¹ (T4). These findings suggest that calcium silicate can improve soil productivity and agronomic efficiency in rice farming. Greenhouse and field trials are necessary to ascertain the effects of the recommended treatments of this incubation study on soil productivity, rice growth, and yield. Full article

This paper presents a comprehensive study of the performance of a newly developed titania nanotube incorporated RO membrane for endocrine-disrupting compound (EDC) removal at a low concentration. EDCs are known as an emerging contaminant, and if these pollutants are not properly removed, they can enter the water cycle and reach the water supply for residential use, causing harm to human health. Reverse osmosis (RO) has been known as a promising technology to remove EDCs. However, there is a lack of consensus on their performance, especially on the feed concentrations of EDC that vary from one source to another. In this study, polyamide thin-film composite (PA TFC) membrane was incorporated with one-dimensional titania nanotube (TNT) to mitigate trade-off between water permeability and solute rejection of EDC. The characterization indicated that the membrane surface hydrophilicity has been greatly increased with the presence of TNT. Using bisphenol A (BPA) and caffeine as model EDC, the removal efficiencies of the pristine TFC and thin-film nanocomposite (TFN) membranes were evaluated. Compared to TFC membrane, the membrane modified with 0.01% of TNT exhibited improved permeability of 50% and 49% for BPA and caffeine, respectively. A satisfactory BPA rejection of 89.05% and a caffeine rejection of 97.89% were achieved by the TNT incorporated TFN membranes. Furthermore, the greater hydrophilicity and smoother surface of 0.01 TFN membrane led to lower membrane fouling tendency under long-term filtration. Full article

The challenge in the surgical treatment of giant cell tumours of bone is the relatively high recurrence rate after curettage alone. The use of a local adjuvant following curettage, on the other hand, has lowered the rate of recurrence. This systematic review and meta-analysis aimed to investigate the prevalence and risk of local recurrence of giant cell tumours of the bone after cryosurgery and the subsequent complications. Web of Science, Scopus, ScienceDirect, PubMed, and Google Scholar were searched to identify articles published until 13 October 2021. A random-effects model was used to examine the pooled prevalence and risk ratio (RR) of local recurrence in patients with giant cell tumours after cryosurgery with 95% confidence intervals (CIs). This study was registered with PROSPERO (CRD42020211620). A total of 1376 articles were identified, of which 38 studies (n = 1373, 46.2% male) were included in the meta-analysis. Following cryosurgery, the pooled prevalence of local recurrence in giant cell tumours was estimated as 13.5% [95% CI: 9.3–17.8, I² = 63%], where European subjects exhibited the highest prevalence (24.2%). Compared to other local adjuvants. The RR of local recurrence following cryosurgery was 0.85 (95% CI: 0.63–1.17, I² = 15%), which was not statistically significant compared to other local adjuvants. We found 3.9% fracture, 4.0% infection, 2.1% nerve injury, and 1.5% skin necrosis as the common complications. Based on the sensitivity analyses, this study is robust and reliable. This meta-analysis estimated a low prevalence of local recurrence of giant cell tumours with low complications following cryosurgery. Thus, it can be one of the adjuvant options for treating giant cell tumours. Full article

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%. Full article

Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine. Full article

Thyroid hormones have a catabolic effect on bone homeostasis. Hence, this study aimed to evaluate serum vitamin D, calcium, and phosphate and bone marker levels and bone mineral density (BMD) among patients with different thyroid diseases. This cross-sectional study included patients with underlying thyroid diseases (n = 64, hyperthyroid; n = 53 euthyroid; n = 18, hypothyroid) and healthy controls (n = 64). BMD was assessed using z-score and left hip and lumbar bone density (g/cm²). The results showed that the mean serum vitamin D Levels of all groups was low (<50 nmol/L). Thyroid patients had higher serum vitamin D levels than healthy controls. All groups had normal serum calcium and phosphate levels. The carboxy terminal collagen crosslink and procollagen type I N-terminal propeptide levels were high in hyperthyroid patients and low in hypothyroid patients. The z-score for hip and spine did not significantly differ between thyroid patients and control groups. The hip bone density was remarkably low in the hyperthyroid group. In conclusion, this study showed no correlation between serum 25(OH)D levels and thyroid diseases. The bone markers showed a difference between thyroid groups with no significant difference in BMD. Full article