Blood safety is a major global issue. Transfusion transmitted parasitic infections (TTPI) like malaria are rare and possibly under-reported, a situation which could be attributed to lack of awareness of the mosquito-borne transmission of infection. Such infections are still considered potential health hazards, as they can pose a significant threat especially in immunocompromised patients, where they have proven to be fatal. Prevention of the transmission depends solely on the donor’s questionnaire which addresses previous or current infection with aetiologic agents. Donor deferral is effective however clear guidelines are needed. This case report features the transfusion-transmitted of Plasmodium Falciparum in a 15-year-old splenectomised patient with underlying beta thalassaemia major.
Introduction: Increased monocyte percentage and monocyte anisocytosis were suggested as new markers for den- gue fever detection. This study aims to investigate and evaluate monocyte volume standard deviation (MoV-SD) and monocyte percentage (Mono %) parameters using Coulter automated haematology analyser as screening parameters in discriminating between dengue infection and other febrile illness. Methods: A cross-sectional laboratory analysis using suspected dengue fever patients were included in this study. The study was conducted in the Department of Pathology, Hospital Tuanku Jaafar Seremban from June 2016 until June 2017. Patients were classified into dengue positive and dengue negative based on dengue IgM and NS1 result. The diagnostic performance of MoV-SD and Mono % was analysed by receiver operating characteristic (ROC) curve analysis. The cut-off value of the MoV-SD and Mono % was determined and evaluated with the validation group. Chi-square test was used to assess the as- sociation between the parameters. Results: 88 (48.4%) from 182 samples were confirmed to have dengue infection. ROC curve analysis showed Mono % at cut off value of 10.5 % with area under the curve (AUC) of 0.869 with 84.1% sensitivity and 84% specificity (95% CI: 0.812-0.925) and MoV-SD cut off value at 22.2 (AUC 0.776, 80.7% sensitivity, 61.7% specificity, 95% CI: 0.709-0.843) are an excellent parameters in separating dengue positive and dengue-negative patients. A cut-off value of 10.5 of Mono % and 22.2 of MoV-SD were applied to the validation group showed 83.1%, 66.4% sensitivity and 84.9%, 77.3% specificity respectively. Conclusion: MoV-SD and Mono
% parameters are a potential parameter for the screening of dengue infection in acute febrile illness patients with good specificity and sensitivity.
In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent.
An investigative study was carried out in Langat River to determine the heavy metal pollution in the sediment with 22 sampling stations selected for the collection of sediment samples. The sediment samples were digested and analyzed for extractable metal ((48)Cd, (29)Cu, (30)Zn, (33)As, (82)Pb) using the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Parameters, such as pH, Eh, electrical conductivity (EC), salinity, cation exchange capacity (CEC) and loss on ignition (LOI) were also determined. The assessment of heavy metal pollution was derived using the enrichment factors (EF) and geoaccumulation index (I(geo)). This study revealed that the sediment is predominantly by As > Cd > Pb > Zn > Cu. As recorded the highest EF value at 187.45 followed by Cd (100.59), Pb (20.32), Zn (12.42) and Cu (3.46). This is similar to the I(geo), which indicates that the highest level goes to As (2.2), exhibits moderately polluted. Meanwhile, Cd recorded 1.8 and Pb (0.23), which illustrates that both of these elements vary from unpolluted to moderately polluted. The Cu and Zn levels are below 0, which demonstrates background concentrations. The findings are expected to update the current status of the heavy metal pollution as well as creating awareness concerning the security of the river water as a drinking water source.
To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.