Search Results (464)

Background: High-sensitivity cardiac troponin (hs-cTn) is useful for detecting acute myocardial infarction, but chronic hemodialysis patients often have elevated baseline levels that exceed the upper reference limit (URL). This study aimed to determine whether hs-cTnI levels in asymptomatic hemodialysis patients exceed the URL established for the general population, evaluate the impact of high-flux hemodialysis on hs-cTnI concentrations, and examine associations between hs-cTnI levels and subsequent hospitalization or mortality. Methods: A prospective, single-center cohort study was conducted at a tertiary care center from August 2023 to July 2024. Blood samples for hs-cTnI were collected from asymptomatic hemodialysis patients aged ≥ 40 years, measured before and after dialysis within one month. Patients were followed for up to 12 months. Results: Fifty-six patients were enrolled. The mean hs-cTnI levels were 28.4 ng/L pre-dialysis and 27.9 ng/L post-dialysis, with ranges of <6–223 ng/L and <6–187 ng/L, respectively. The mean hs-cTnI delta between pre- and post-dialysis was −0.5 ng/L, with 52% showing a negative delta, 30% no change, and 18% a positive delta. No association was found between baseline hs-cTnI levels and mortality or hospitalization during follow-up. Conclusions: Most asymptomatic hemodialysis patients had hs-cTnI levels in the “gray zone”, thus neither confirming nor excluding acute myocardial infarction. Dialysis did not significantly affect hs-cTnI levels, and elevated baseline hs-cTnI was not linked to increased mortality or hospitalization over 12 months. Full article

In pursuit of the Sustainable Development Goals (SDGs)’s objective of eliminating slum cities, the government of Indonesia has initiated a survey-based slum mapping program. Unfortunately, recent observations have highlighted considerable inconsistencies in the mapping process. These inconsistencies can be attributed to various factors, including variations in the expertise of surveyors and the intricacies of the indicators employed to characterize slum conditions. Consequently, reliable data is lacking, which poses a significant barrier to effective monitoring of slum upgrading programs. Remote sensing (RS)-based approaches, particularly those employing deep learning (DL) techniques, have emerged as a highly effective and accurate method for identifying slum areas. However, the reliance on RS alone is likely to encounter challenges in complex urban environments. A substantial body of research has previously identified the merits of integrating land surface data with RS. Therefore, this study seeks to combine remote sensing imagery (RSI) with street view imagery (SVI) for the purpose of slum mapping and compare its accuracy with a field survey conducted in 2024. The city of Bandung is a pertinent case study, as it is facing a considerable increase in population density. These slums collectively encompass approximately one-tenth of Bandung City’s population as of 2020. The present investigation evaluates the mapping results obtained from four distinct deep learning (DL) networks: The first category comprises FCN, which utilizes RSI exclusively, and FCN-DK, which also employs RSI as its sole input. The second category consists of two networks that integrate RSI and SVI, namely FCN and FCN-DK. The findings indicate that the integration of RSI and SVI enhances the precision of slum mapping in Bandung City, particularly when employing the FCN-DK network, achieving an accuracy of 86.25%. The results of the mapping process employing a combination of the FCN-DK network, which utilizes the RSI and SVI, indicate the presence of 2294 light slum points and 29 medium slum points. It should be noted that the outcomes are contingent upon the methodological approach employed, the accessibility of the dataset, and the training data that mirrors the distribution of slums in 2020 and the specific degree of its integration within the FCN network. The FCN-DK model, which integrates RSI and SVI, demonstrates enhanced performance in comparison to the other models examined in this study. Full article

Parkinson’s disease (PD) is the second most common neurodegenerative disorder in the world. PD is characterized by motor and non-motor symptoms, but the diagnosis primarily relies on the clinical assessment of postural and movement abnormalities, supported by imaging and genetic testing. It is widely accepted that the disease process begins decades before the onset of overt symptoms. Emerging evidence suggests that neuroinflammation plays a central role in the pathogenesis of PD, particularly during the pre-clinical phase. Activated microglia, increased levels of pro-inflammatory cytokines, and persistent oxidative stress have all been associated with the gradual loss of dopaminergic neurons. Although earlier detection and diagnosis remain elusive, achieving these goals is crucial for advancing prevention and disease-modifying strategies. Clinical studies are ongoing. To fill the gap, research models that recapitulate the chronic disease progression of PD are crucial to test preventive and disease-modifying strategies. This review briefly summarizes clinical knowledge on PD as a starting point for improving research models. Furthermore, we will critically evaluate how the existing models have been utilized and highlight opportunities to overcome their limitations and enhance the translational relevance to clinical application. Full article

Domestic wastewater discharge is the major source of pollution in Malaysia. Phytoremediation under hydroponic conditions was initiated to treat domestic wastewater and, at the same time, to resolve the space limitation issue by installing a hydroponic system in vertical space at the site. Water hyacinth (WH) was selected in this study to identify its performance of water hyacinth in removing nutrients in raw sewage under batch operation. In the batch experiment, the ratio of COD_initial/plant_initial was identified, and SPSS ANOVA analysis shows that the number of plant size factors was not statistically different in this study. Therefore, four WH, each with an initial weight of 60 ± 20 g, were recommended for this study. Throughout the 10 days of the batch experiment, the average of COD, BOD, TSS, TP, NH4, and color removal was 73%, 73%, 86%, 79%, 77%, and 54%, respectively. The WH biomass weight increased by an average of 78%. The plants have also improved the DO level from 0.24 mg/L to 4.88 mg/L. However, the pH of effluent decreased from pH 7.05 to pH 4.88 below the sewage Standard B discharge limit of pH 9–pH 5.50. Four WH plant groups were recommended for future study, as the COD removal among the other plant groups is not a statistically significant difference (p < 0.05). Furthermore, the lower plant biomass is preferable for the high pollutant removal performance due to the fact that it can reduce the maintenance and operating costs. Full article

Logs supplied in Papua New Guinea and Indonesia are predominantly sourced from fast-growing tree species of plantation forests. The timber primarily consists of sapwood, which is highly susceptible to biodeterioration. At a training center, CCA (chromated copper arsenate) is still used for wood preservation, while in the wood industry, ACQ (alkaline copper quaternary) is commonly applied to enhance the service life of timber. In the future, polystyrene impregnation or other non-biocidal treatments could potentially serve this purpose. This study aimed to determine the discoloration and resistance of polystyrene-impregnated and CCA-preserved woods. Wood samples, Anisoptera thurifera and Octomeles sumatrana from Papua New Guinea, and Anthocephalus cadamba and Falcataria moluccana from Indonesia, were used. The wood samples were treated with polystyrene impregnation, CCA preservation, or left untreated, then exposed at the PNG Forest Research Institute site for four months. After treatment, the color change in polystyrene-impregnated wood was minor, whereas CCA-preserved wood exhibited a noticeably different color compared to untreated wood. The average polymer loading for polystyrene-impregnated wood reached 147%, while the average CCA retention was 8.4 kg/m³. Densities of untreated-, polystyrene-, and CCA-wood were 0.42, 0.64, and 0.45 g/cm³, respectively, and moisture contents were 15.8%, 9.4%, and 13.4%, respectively. CCA preservation proved highly effective in preventing termite attacks; however, CCA is hazardous to living organisms, including humans. Polystyrene impregnation also significantly improved wood resistance to subterranean termites, as indicated by lower weight loss and a higher protection level compared to untreated wood. Additionally, polystyrene treatment is nonhazardous and safe for living organisms, making it a promising option for enhancing wood resistance to termite attacks in the future as an alternative to the biocides currently in use. Full article

Hepatocellular carcinoma (HCC) remains a major clinical challenge due to its high recurrence rate and limited response to monotherapies, such as sorafenib—the standard first-line therapy for advanced HCC. This is partly attributed to its cellular heterogeneity. Increasing evidence implies SRC family kinase (SFK) activation in HCC progression, highlighting the potential of SRC-targeted therapies. In this study, we observed that SRC and YES1 were significantly upregulated in clinical HCC specimens compared to its adjacent non-tumoral tissues (p < 0.001), suggesting relevance as therapeutic targets. High SRC expression was noticed in patients with poor prognosis, as confirmed in TCGA cohort. To evaluate the efficacy of dual targeting, we assessed the combination between SRC inhibitors, saracatinib and dasatinib, with sorafenib in six hepatic cell models, representing both S1 and S2 subtypes. Cytotoxicity assays demonstrated reduced cell viability with the combination therapies compared to either monotherapy, irrespective of the HCC subtype. Wound healing and Transwell migration assays revealed inhibition of cell migration and invasion following combination treatment, underscoring its potential to suppress metastatic behavior. RT-qPCR analysis further confirmed downregulation of the expression of MMP2 and MMP9, genes associated with HCC cell invasion. Additionally, combined therapies decreased VEGFA and HIF1A expression compared to sorafenib alone, suggesting a potential to counteract the adaptive resistance mechanisms of cells to sorafenib. In summary, the combination of SFK inhibitors with sorafenib significantly enhances anti-tumor activity, offering a promising strategy to address HCC cellular heterogeneity and improve treatment efficacy. Full article

Indonesia faces significant challenges in meeting food security targets due to rapid agricultural land loss, with approximately 1.22 million hectares of rice fields converted between 1990 and 2022. Therefore, this study developed a prediction model for the loss of rice fields by 2030, incorporating land productivity attributes, specifically rice cropping intensity/RCI, using geospatial technology—a novel method with a resolution of approximately 10 m for quantifying ecosystem service (ES) impacts. Land use/land cover data from Landsat images (2013, 2020, 2024) were classified using the Random Forest algorithm on Google Earth Engine. The prediction model was developed using a Multi-Layer Perceptron Neural Network and Markov Cellular Automata (MLP-NN Markov-CA) algorithms. Additionally, time series Sentinel-1A satellite imagery was processed using K-means and a hierarchical clustering analysis to map rice fields and their RCI. The validation process confirmed high model robustness, with an MLP-NN Markov-CA accuracy and Kappa coefficient of 83.90% and 0.91, respectively. The present study, which was conducted in Indramayu Regency (West Java), predicted that 1602.73 hectares of paddy fields would be lost within 2020–2030, specifically 980.54 hectares (61.18%) and 622.19 hectares (38.82%) with 2 RCI and 1 RCI, respectively. This land conversion directly threatens ES, resulting in a projected loss of 83,697.95 tons of rice production, which indicates a critical degradation of service provisioning. The findings provide actionable insights for land use planning to reduce agricultural land conversion while outlining the urgency of safeguarding ES values. The adopted method is applicable to regions with similar characteristics. Full article

The accurate and timely estimation of earthquake magnitude is essential for effective tsunami early warning, particularly in seismically active regions such as Indonesia. Conventional seismic approaches are often hindered by magnitude saturation in significant events (Mw > 7.5), resulting in systematically underestimated magnitudes. To address this limitation, we develop a regional peak ground displacement (PGD) scaling law using high-rate GNSS (HR-GNSS) data from 21 moderate to large earthquakes in Indonesia. Based on 87 PGD observations, we construct a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) exhibit strong concordance with catalog moment magnitudes, achieving a mean absolute deviation (MAD) of 0.21 and surpassing the accuracy of previously published global models. Retrospective analyses reveal that MPGD estimates converge within 2–3 min for well-recorded events and remain robust, even for great and tsunamigenic earthquakes. These results underscore the potential of HR-GNSS data to complement conventional seismic networks, providing rapid and reliable magnitude estimates for operational tsunami early warning in Indonesia. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

Rapid and accurate detection of primary waves (P-waves) using high-rate Global Navigation Satellite System (GNSS) data is essential for earthquake monitoring and tsunami early warning systems, where traditional seismic methods are less effective in noisy environments. We applied a wavelet-based method using a Mexican hat wavelet and dynamic threshold to thoroughly analyze the three-component displacement waveforms of the 2009 Padang, 2012 Simeulue, and 2018 Palu Indonesian earthquakes. Data from the Sumatran GPS Array and Indonesian Continuously Operating Reference Stations were analyzed to determine accurate displacements and P-waves. Validation with Indonesian geophysical agency seismic records indicated reliable detection of the horizontal component, with a time delay of less than 90 s, whereas the vertical component detection was inconsistent, owing to noise. Spectrogram analysis revealed P-wave energy in the pseudo-frequency range of 0.02–0.5 Hz and confirmed the method’s sensitivity to low-frequency signals. This approach illustrates the utility of GNSS data as a complement to seismic networks for the rapid characterization of earthquakes in complex tectonic regions. Improving the vertical component noise suppression might further help secure their utility in real-time early warning systems. Full article

Rapid advancements in artificial intelligence (AI) have led to a substantial increase in energy consumption, particularly during the training phase of AI models. As AI adoption continues to grow, its environmental impact presents a significant challenge to the achievement of the United Nations’ Sustainable Development Goals (SDGs). This study examines how three key training configuration parameters—early-stopping epochs, training data size, and batch size—can be optimized to balance model accuracy and energy efficiency. Through a series of experimental simulations, we analyze the impact of each parameter on both energy consumption and model performance, offering insights that contribute to the development of environmental policies that are aligned with the SDGs. The results demonstrate strong potential for reducing energy usage without compromising model reliability. The results highlight three lessons: promoting early-stopping epochs as an energy-efficient practice, limiting training data size to enhance energy efficiency, and developing standardized guidelines for batch size optimization. The practical applicability of these three lessons is illustrated through the implementation of a smart building attendance system using facial recognition technology within an Ecocampus environment. This real-world application highlights how energy-conscious AI training configurations support sustainable urban innovation and contribute to climate action and environmentally responsible AI development. Full article

Saninten (Castanopsis argentea (Blume) A.DC.) is a protected plant that grows in the Mount Gede Pangrango National Park (MGPNP) area in West Java. Its population is limited, and as a valuable biological resource, Castanopsis has traditionally been utilized by indigenous communities, particularly those residing in proximity to the forest. However, the expansion and development of tourism pose a potential threat to the ecosystems of C. argentea and other endemic plant species, as well as to the wildlife that depend on these habitats. Comprehensive data on biodiversity, species composition, forest structure, and carbon stock status are crucial for assessing the potential impact of future tourism development. Our investigation was conducted from November 2023 to March 2024 in a three-hectare utilization zone within the confines of the national park. The findings documented a total of 36 species across 23 distinct plant families, with the families Fagaceae, Moraceae, and Myrtaceae exhibiting the highest levels of dominance. The regeneration of stands at the study site predominantly comprised arboreal species with the most substantial carbon stocks, including C. acuminatissima (Blume) A.DC. (Riung anak), C. argentea (Saninten), and Litsea sp. (Huru). C. argentea supplies several functions within this ecosystem that are interconnected with other components. With aboveground carbon stocks reaching 560.47 tons C/ha, the forest demonstrates high sequestration potential, reinforcing the need to conserve mature stands for both biodiversity and climate benefits. Therefore, in the future, the conservation of C. argentea will benefit the maintenance of the ecosystem’s attractiveness without adversely affecting the social and cultural structures of the local population. Full article

Background: Takotsubo syndrome (TS) mimics acute coronary syndrome in 1% to 3% of patients presenting with chest pain, ECG changes and echocardiographic transient apical wall hypokinesia. Objectives: This study aimed to validate a previously developed scoring system on a larger cohort size. Methods: Patients admitted to an intensive cardiovascular care unit were divided into three groups: (a) patients diagnosed with TS, (b) females with anterior MI, and (c) other all-comer STEMIs. A 10-point scoring system was used: stressful events (three points), female gender (two points), no history of diabetes mellitus (two points), estimated left ventricular ejection fraction (LVEF) ≤ 40% on admission echocardiography (one point), positive troponin on admission (one point), and no smoking (one point). A t-test was applied to the three study groups, sensitivity and specificity testing was performed using the ROC curve method. Results: A total of 1150 patients were included in our study: 54 with TS, 97 females with anterior MI and 999 other all-comer STEMIs. Patients in the TS group were predominantly females with a higher rate of stressful events prior to admission, lower rates of diabetes mellitus and smoking, and lower LVEF% systolic function compared to the STEMI cohort. In a multivariate logistic regression analysis, the average TS scoring system was significantly higher in the TS group compared with the anterior STEMI and all-comer STEMI groups (8.3 vs. 5.7 vs. 3.83, p < 0.001, respectively) with an AUC of 0.83 for TS score ≥ 8. Conclusions: The 10-point TS scoring system is an easy, reliable, and useful diagnostic tool that might help in distinguishing patients with TS and ACS. Full article

This article explores the maritime connections relating to Buddhism and diplomacy between polities in Sumatra, the Malay Peninsula, the Indian subcontinent, and China from the beginning of the 11th century up to the 12th century CE. It focuses on new epigraphic evidence from Muara Jambi in the form of two inscribed strips of copper mentioning the Cūḍāmaṇivarmavihāra, a monastery funded by the king of Śrīvijaya in Nagapattinam (South India), and the Bālādityavihāra, probably located in Nālandā (Northeastern India). These new findings are compared to archaeological and textual materials from elsewhere in the Buddhist world that cast light on the web of transregional connections between Nusantara, China, and India in the early centuries of the second millennium. Full article

The Mamuju region in West Sulawesi, Indonesia, is a High Background Natural Radiation Area (HBNRA) characterized by a significant enrichment of high field strength elements (HFSEs) and rare earth elements (REEs) within its lateritic deposits. This study investigates the geochemical behavior, mineralogical distribution, and enrichment processes of HFSEs and REEs in lateritic profiles of drill cores and surface samples derived from alkaline volcanic rocks. The mineralogy and geochemical content of HFSEs and REEs in the alkaline bedrocks indicate its potential to become a source of lateritic enrichment. An intense lateritic weathering process leads to the residual accumulation of HFSEs and REEs, particularly in B-horizon soils, where clay minerals and Fe–Al oxides are crucial in element precipitation. Moreover, groundwater redox conditions are a key factor for uranium precipitation in the lateritic profile. The findings provide insight into the potential of lateritic weathering as a natural mechanism for HFSE and REE concentration, contributing to the broader understanding of critical metal resources in Indonesia. These insights have implications for sustainable resource exploration and environmental management in areas with high natural radiation exposure. Full article