Search Results (44)

GW4869, a cell-permeable, selective inhibitor of neutral sphingomyelinase is a pharmacological agent that blocks the production and release of extracellular vesicles (EVs). Our previous studies have shown that GW4869 inhibits flaviviral loads in tick, mosquito and mammalian cells, including murine cortical neurons. Yet the mechanism(s) of GW4869 inhibitor upon viral infections were not addressed. In the current study, we focused on how GW4869 interferes with Langat Virus (LGTV, a tick-borne flavivirus) replication in ISE6 tick cells. First, we found that GW4869 is neither cytotoxic at tested doses of 50, 100, and 150 µM in tick cells, nor does it directly bind to the free LGTV present in cell culture supernatants. When tick cells were treated with GW4869, followed by infection with viral stock at dilutions of 10⁻², 10⁻³, 10⁻⁴ (the infectious dose determination by viral dilution assay), it affected LGTV replication in tick cells. A reduction in viral burden was noted in GW4869-treated tick cells, which constituted more than half the amount of decrease when compared to the mock control. Next, GW4869 treatment not only resulted in decreased LGTV transcript levels in tick cells and EVs derived from these infected cells, but also revealed diminished EVs concentrations. Enhanced IsSMase transcripts in the LGTV-infected group was noted upon GW4869 treatment, thus suggesting a host response to perhaps inhibit virus replication. In addition, GW4869 treatment reduced LGTV loads in density gradient EVs fractions, which correlated with decreased EVs concentration in those fractions. These data not only indicate that GW4869 affects LGTV replication, but that it also interferes with EV secretion and release from tick cells. Lastly, we found that GW4869 inhibits LGTV replication in tick cells but does not directly affect the infectivity of LGTV viral particles. Overall, our study suggests that GW4869 is a potential therapeutic inhibitor in controlling tick-borne diseases. Full article

Flaviviruses are usually transmitted to humans via mosquito or tick bites, whose infections may lead to severe diseases and fatality. During intracellular infection, they remodel the endoplasmic reticulum (ER) membrane to generate compartments scaffolding the replication complex (RC) where replication of the viral genome takes place. In this study, we purified the ER membrane fraction of virus infected cells to identify the proteins that were enriched during flavivirus infection. We found that tripartite motif-containing proteins (TRIMs) including TRIM38, TRIM21, and TRIM14 were significantly enriched during infection with mosquito-borne (West Nile virus strain Kunjin and Zika virus (ZIKV)) and tick-borne (Langat virus (LGTV)) flaviviruses. Further characterizations showed that TRIM21 and TRIM14 act as restriction factors against ZIKV and LGTV, while TRIM38 hinders ZIKV infection. These TRIMs worked as interferon-stimulated genes to mediate IFN-I response against LGTV and ZIKV infections. Restriction of ZIKV by TRIM14 and TRIM38 coincides with their colocalization with ZIKV NS3. TRIM14-mediated LGTV restriction coincides with its colocalization with LGTV NS3 and NS5 proteins. However, TRIM21 did not colocalize with ZIKV and LGTV NS3 or NS5 protein suggesting its antiviral activity is not dependent on direct targeting the viral enzyme. Finally, we demonstrated that overexpression of TRIM21 and TRIM14 restricted LGTV replication. Full article

Tick-borne encephalitis remains a significant burden on human health in the endemic areas in Central Europe and Eastern Asia. The causative agent, tick-borne encephalitis virus (TBEV), is a neurotropic virus belonging to the genus of Orthoflavivirus. After TBEV enters the central nervous system (CNS), it mainly targets neurons, causing encephalitis and leading to life-long disabilities, coma and, in rare cases, death. The neuroinvasive mechanisms of orthoflaviviruses are poorly understood. Here we investigate the mechanism of TBEV neuroinvasion, hypothesizing that TBEV influences blood–brain barrier (BBB) properties and uses transcellular routes to cross the endothelial barrier and enter the CNS. To test this hypothesis, we employed an in vitro transwell system consisting of endothelial cell monolayers cultured on insert membranes and studied the barrier properties following inoculation to tick-borne orthoflaviviruses. It was shown that TBEV and closely related but naturally attenuated Langat virus (LGTV) crossed the intact endothelial cell monolayer without altering its barrier properties. Interestingly, transendothelial migration of TBEV was significantly affected when two cellular surface antigens, the laminin-binding protein and vimentin, were blocked with specific antibodies. Taken together, these results indicate that orthoflaviviruses use non-destructive transcellular routes through endothelial cells to establish infection within the CNS. Full article

Tick-borne encephalitis virus (TBEV) is a neurotropic member of the genus Orthoflavivirus (former Flavivirus) and is of significant health concern in Europe and Asia. TBEV pathogenesis may occur directly via virus-induced damage to neurons or through immunopathology due to excessive inflammation. While primary cells isolated from the host can be used to study the immune response to TBEV, it is still unclear how well these reflect the immune response elicited in vivo. Here, we compared the transcriptional response to TBEV and the less pathogenic tick-borne flavivirus, Langat virus (LGTV), in primary monocultures of neurons, astrocytes and microglia in vitro, with the transcriptional response in vivo captured by single-nuclei RNA sequencing (snRNA-seq) of a whole mouse cortex. We detected similar transcriptional changes induced by both LGTV and TBEV infection in vitro, with the lower response to LGTV likely resulting from slower viral kinetics. Gene set enrichment analysis showed a stronger transcriptional response in vivo than in vitro for astrocytes and microglia, with a limited overlap mainly dominated by interferon signaling. Together, this adds to our understanding of neurotropic flavivirus pathogenesis and the strengths and limitations of available model systems. Full article

Haemaphysalis longicornis (the longhorned tick), the predominant tick species in China, serves as a vector for a variety of pathogens, and is capable of transmitting the tick-borne encephalitis virus (TBEV), the causative agent of tick-borne encephalitis. However, it is unclear how these ticks transmit TBEV. Langat virus (LGTV), which has a reduced pathogenicity in humans, has been used as a surrogate for TBEV. In this study, we aimed to investigate the vector competence of H. longicornis to transmit LGTV and demonstrate the efficient acquisition and transmission of LGTV between this tick species and mice. LGTV localization was detected in several tick tissues, such as the midgut, salivary glands, and synganglion, using quantitative PCR and immunohistochemical staining with a polyclonal antibody targeting the LGTV envelope protein. We demonstrated the horizontal transmission of LGTV to different developmental stages within the same generation but did not see evidence of vertical transmission. It was interesting to note that we observed mice acting as a bridge, facilitating the transmission of LGTV to neighboring naïve ticks during blood feeding. In conclusion, the virus–vector–host model employed in this study provides valuable insights into the replication and transmission of LGTV throughout its life cycle. Full article

The Asian palm civet (APC), Paradoxurus hermaphroditus, is a native Malaysian mammal, and recently, it has increasingly caused conflicts with humans as it ventures into local settlements for food. A study surveying 212 locals and analyzing the APC scats was conducted in Hulu Langat, Selangor, Malaysia, from August 2021 to December 2022 to understand the coexistence potential. The findings show: (1) The conflicts mainly arise due to the APCs’ foraging habits. (2) APCs cause local damage, including cultivated fruit consumption, poultry predation, and agricultural and property damage. (3) Most locals have a positive attitude toward APCs, although, in local settlements, they are considered to be pests. Respondents who experienced losses of cultivated fruits and poultry, and were familiar with APCs, had more negative attitudes. (4) Most locals believe that the APC population has increased over the past decade. (5) Only a few locals actively engage in mitigating the conflict through the use of poison, while most of them do not take any action. Although Malaysia’s human–Asian palm civet conflict is relatively tolerant, prioritizing management strategies is crucial. Conservation practitioners must address these conflicts by highlighting the need for further research and a holistic approach considering social, economic, and ecological factors. Full article

This paper investigates the possibilities of creating magnetic field sensors using the direct magnetoelectric (ME) effect in a monolithic heterostructure of amorphous ferromagnetic material/langatate. Layers of 1.5 μm-thick FeCoSiB amorphous ferromagnetic material were deposited on the surface of the langatate single crystal using magnetron sputtering. At the resonance frequency of the structure, 107 kHz, the ME coefficient of linear conversion of 76.6 V/(Oe∙cm) was obtained. Furthermore, the nonlinear ME effect of voltage harmonic generation was observed with an increasing excitation magnetic field. The efficiency of generating the second and third harmonics was about 6.3 V/(Oe²∙cm) and 1.8 V/(Oe³∙cm), respectively. A hysteresis dependence of ME voltage on a permanent magnetic field was observed due to the presence of α-Fe iron crystalline phases in the magnetic layer. At the resonance frequency, the monolithic heterostructure had a sensitivity to the AC magnetic field of 4.6 V/Oe, a minimum detectable magnetic field of ~70 pT, and a low level of magnetic noise of 0.36 pT/Hz^1/2, which allows it to be used in ME magnetic field sensors. Full article

The genetic diversities of mammalian tick-borne flaviviruses are poorly understood. We used next-generation sequencing (NGS) to deep sequence different viruses and strains belonging to this group of flaviviruses, including Central European tick-borne encephalitis virus (TBEV-Eur), Far Eastern TBEV (TBEV-FE), Langat (LGTV), Powassan (POWV), Deer Tick (DTV), Kyasanur Forest Disease (KFDV), Alkhurma hemorrhagic fever (AHFV), and Omsk hemorrhagic fever (OHFV) viruses. DTV, AHFV, and KFDV had the lowest genetic diversity, while POWV strains LEIV-5530 and LB, OHFV, TBEV-Eur, and TBEV-FE had higher genetic diversities. These findings are compatible with the phylogenetic relationships between the viruses. For DTV and POWV, the amount of genetic diversity could be explained by the number of tick vector species and amplification hosts each virus can occupy, with low diversity DTV having a more limited vector and host pool, while POWV with higher genetic diversities has been isolated from different tick species and mammals. It is speculated that high genetic diversity may contribute to the survival of the virus as it encounters these different environments. Full article

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB. Full article

Flaviviruses are a heterogeneous group of viruses that may induce broad antigenic cross-reactivity. We present a patient who was admitted to the infectious disease department with symptoms suggestive of aseptic meningitis. During the clinical workup, the patient reported a tick bite two weeks before the disease onset. High titers of IgM and IgG antibodies to tick-borne encephalitis virus (TBEV) were found in both serum and cerebrospinal fluid (CSF) samples, indicating acute TBEV infection. West Nile virus (WNV) and Usutu virus (USUV) IgM and/or IgG antibodies were also detected, and a virus neutralization test (VNT) was performed. A high titer of TBEV neutralizing (NT) antibodies (640) was detected, which confirmed acute TBE. However, NT antibodies to WNV and USUV were also detected (titer 80 for both viruses). After TBEV and WNV IgG avidity evaluation, previous flavivirus infection was highly suspected (avidity index 82% and 89%, respectively). Blood, CSF, and urine samples were negative for respective viruses’ RNA. The presented case highlights the challenges in flavivirus serodiagnosis. In the published literature, different degrees of cross-reactivity or cross-neutralization between TBEV and dengue, louping ill, Omsk hemorrhagic fever, Langat, and Powassan virus were also observed. Therefore, the serology results should be interpreted with caution, including the possibility of cross-reactivity. In areas where several flaviviruses co-circulate VNT is recommended for disease confirmation. Full article

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula. Full article

Disasters in Malaysia are managed using a framework developed through Directive 20 by the National Security Council. This framework is widely used in managing floods on the East Coast of Peninsular Malaysia. However, the prolonged rains that occurred on 17 December 2021 tested the capabilities of the existing framework: the rains caused floods in urban areas, which is unusual. This study was conducted to investigate the flood situation using data from the media, observations of the affected area, and people’s perceptions to determine their actions upon receiving flood information from the media. This study used thematic analysis to analyze the media content on the floods in Selangor. Next, observation techniques were used in one of the most affected areas, namely Hulu Langat, Selangor, where content analysis of field notes was implemented to determine the emerging themes that were being formed. Finally, an online survey questionnaire was distributed through social media. This study’s findings established that what was reported in the media was correct; however, what actually occurred was worse than what was stated in the media. Through the survey, it was found that people are extremely reliant on social media and assume that logistical constraints in the delivery of assistance have contributed to negative public perceptions of disaster management agencies. Full article

Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R²). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research conducted to predict TS and TSS in Langat River. Therefore, this is the first study to evaluate the water quality (TSS, TS, and DS) of Langat River using the aforementioned models (especially SVM and the six regression models). Full article

The shutdown of drinking water treatment plants (DWTPs) at the Langat River Basin, Malaysia, which provides drinking water to almost one-third population in the basin, is very frequent, especially due to chemical pollution in the river. This study explored the pollution sources in the Langat River based on eight specific water intake points of the respective DWTPs to suggest an integrated river basin management (IRBM). Analysis of Al (250.26 ± 189.24 µg/L), As (1.65 ± 0.93 µg/L), Cd (1.22 ± 0.88 µg/L), Cr (0.47 ± 0.27 µg/L), and Pb (9.99 ± 5.38 µg/L) by ICP-MS following the Chelex^® 100 column resin ion exchange method found that the mean concentrations except Al were within the water quality standard of the Ministry of Health (MOH) as well as the Dept. of Environment (DOE) Malaysia. However, the determined water quality index based on physicochemical parameters (2005–2015) at the midstream of Langat River was Class III, which needs substantial treatment before drinking. The linear regression model of Al, As, Cd, and Pb suggests that water quality parameters are significantly influencing the increase or decrease in these metal concentrations. Moreover, the principal component analysis (PCA) and the hierarchical cluster analysis (HCA) also support the regression models that the sources of pollution are both natural and man-made activities, and these pollution sources can be clustered into two categories, i.e., upstream (category 1) and mid to downstream (category 2) in the Langat River. The degraded water quality in the midstream compared to up and downstream of the river is mainly due to human activities apart from the natural weathering of minerals. Therefore, the implementation of policies should be effective at the local level for pollution management, especially via the proactive leadership roles of local government for this transboundary Langat River to benefit from IRBM. Full article

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin. Full article