Search Results (3,764)

SARS-CoV-2, the causative virus of the COVID-19 pandemic, is a highly transmissible, enveloped, single-stranded RNA virus that has mutated into several variants, complicating vaccine strategies and drug resistance. Novel treatment modalities targeting conserved structural vulnerable points are essential to combat these variants. The primary aim of the current study is to test the mechanical vulnerability of the SARS-CoV-2 virus envelope and spike proteins to focused, high-frequency ultrasound waves (25 MHz) in vitro. Utilizing a preliminary pretest and posttest study design, the study was conducted on a virus sample within a distilled water matrix, under controlled laboratory biosafety conditions. Since detailed imaging tools were unavailable, viral disruption was indirectly measured using real-time PCR cycle threshold (Ct) values. Ct values increased significantly after high-frequency ultrasound exposure, indicating a reduction in amplifiable viral genomic material. A paired t-test indicated a significant difference between the pretest and posttest Ct (p < 0.001), which is supported by Monte Carlo test results that revealed statistically significant shifting in viral load categories (p = 0.001, two-sided). Specifically, 85.7% of high-viral-load samples converted to low or moderate content, 46.7% of low or moderate samples were shifted to negative content. This intervention produced a large effect size (Cohen’s d = 2.422). These results indicate that ultrasound may offer a promising non-pharmacological approach to destroy or inactivate SARS-CoV-2 variants in an aqueous environment. Full article

The black soil region of Northeast China (NEC) is China’s most important food production base. Long-term inefficient land use has made its ecosystem vulnerable to widespread degradation, prompting the implementation of ecological restoration projects (ERPs) to enhance ecosystem service (ES) resilience. Yet, the complex interactions among key ESs, including grain production (GP), water yield (WY), soil conservation (SC), and carbon storage (CS), as well as the spatial non-stationarity of their driving factors post-ERPs, have caused spatially heterogeneous, scale-dependent ES relationships. To address these gaps, this study aims to analyze temporal changes in ESs across multiple scales in NEC from 2000 to 2020. By mapping the interactions and quantifying their intensities, we revealed spatial variations in driving factors under different ERPs. The results show that the Natural Wetland Conservation Project (NWCP) and Three-North Shelterbelt Program (TNSP) have led to overall improvements in all ESs. In contrast, the Grain for Green Program (GFGP), the Land Salinity/Sodicity Amelioration Project (LASP), and the Natural Forests Conservation Program (NFCP) are associated with trade-offs between ESs. Interactions between ESs exhibited clear spatial scale dependence, and the dominant drivers varied across scales and restoration contexts. These findings highlight the importance of considering spatial scale and non-stationarity when evaluating ecological restoration outcomes. This study provides a scientific basis for the development and management of ecological restoration programs in intensively managed agricultural regions worldwide, particularly those undergoing multiple, overlapping restoration interventions, from a multi-scale spatial perspective. Full article

Ischemic heart disease (IHD) remains a leading cause of morbidity and mortality worldwide. Myocardial ischemia–reperfusion injury (MIRI) is a significant contributor to cardiac tissue damage, resulting from an abrupt reduction in blood flow that leads to a reduction in the supply of oxygen and nutrients. The resulting hypoxia triggers severe cellular injury and impairs organ function. Hypoxia-inducible factors (HIFs) play a central role in maintaining oxygen homeostasis in mammalian tissues. As primary oxygen sensors, HIFs trigger the transcriptional activation of a wide range of genes that facilitate cellular adaptation to reduced oxygen availability and assist in minimizing ischemic damage. Mitochondria are particularly vulnerable to hypoxic stress and are a major source of reactive oxygen species (ROS) during I/R injury. Stabilization of HIFs has been shown to reduce loss of cardiomyocytes under these conditions, highlighting the importance of HIF-dependent pathways in preserving mitochondrial integrity and promoting cell survival. Collectively, these observations suggest that hypoxia, HIF signaling, and mitochondrial dysfunction are tightly interconnected processes in the pathogenesis of IHD. This review, therefore, focuses on the interaction between hypoxia-driven HIF responses and mitochondrial regulation, emphasizing their implications for therapeutic strategies in managing IHD. Full article

A growing body of work has linked the dysregulation of transmembrane (TMEM) proteins to the proliferation, metastasis, drug resistance, and tumor microenvironment remodeling of lung cancer, the leading global cause of cancer mortality. Renamed members such as STING1 (stimulator of interferon response cGAMP interactor 1, TMEM173), ANO1 (anoctamin-1, TMEM16A), ORAI1 (ORAI calcium release-activated calcium modulator 1, TMEM142A), ORAI3 (TMEM142C), and NDC1 (NDC1 transmembrane nucleoporin, TMEM48) are among the most extensively studied ones. Mechanisms of TMEM dysregulation in lung cancer span the modulation of Ca²⁺ influx, lysosomal exocytosis, ferroptosis, Wnt and β-catenin signaling, and immune cell infiltration and immune checkpoint rewiring, among others. Epigenetic silencing and targetable fusions (i.e., TMEM106B-ROS1 and TMEM87A-RASGRF1) create DNA-level vulnerabilities, while miRNA sponges offer RNA-level druggability. A subset of studies revealed context-specific expression (endothelial, B cell, and hypoxic EV) that can be exploited to remodel the tumor microenvironment. One study specifically focused on how isoform-specific expression and localization of TMEM88 determine its functional impact on tumor progression. Yet for most TMEMs, only pre-clinical or early-phase data exist, with many supported by a single study lacking independent validation. This review brings together scattered evidence on TMEM proteins in lung cancer, with the aim of guiding future work on their possible use as biomarkers or therapeutic targets. Full article

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

The intensive use of pesticides in agriculture poses serious risks to aquatic ecosystems and non-target organisms, yet toxicological data remain limited. This study evaluated the acute effects of three widely used pesticides—pirimiphos-methyl (10 and 60 μg/L), propamocarb hydrochloride (40 and 80 μg/L), and 2,4-D (50 and 100 μg/L)—on the liver of common carp (Cyprinus carpio Linnaeus, 1758), a sentinel species in aquaculture, but also a species equally important in risk assessment and environmental monitoring. Fish were exposed for 96 h under controlled conditions, and histopathological, histochemical, and biochemical biomarkers were analyzed. All tested pesticides induced significant histopathological alterations, predominantly circulatory and degenerative changes, with severity increasing at higher concentrations. Propamocarb hydrochloride and 2,4-D caused more pronounced and partly irreversible hepatotoxicity compared to pirimiphos-methyl. The histochemical assessment revealed altered glycogen metabolism, while the biochemical assays showed inhibition of key liver enzymes, including ALAT, ASAT, ChE, and LDH, indicating disrupted metabolic processes. These findings highlight the vulnerability of aquatic organisms to pesticide exposure and support the use of fish liver biomarkers as effective tools in ecotoxicology research. The study also emphasizes the need for stricter regulation and environmental monitoring of pesticide contamination in aquatic ecosystems. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Compared with traditional two-dimensional (2D) models, patient-derived CRC organoids more faithfully preserve the genomic, transcriptomic, and architectural features of primary tumors, making them a powerful intermediate platform bridging basic discovery and clinical translation. Over the past several years, organoid systems have rapidly expanded beyond conventional epithelial-only cultures toward increasingly complex architectures, including immune-organoid co-culture models and mini-colon systems that enable long-term, spatially resolved tracking of tumor evolution. These advanced platforms, combined with high-throughput technologies and clustered regularly interspaced short palindromic repeats (CRISPR)-based functional genomics, have substantially enhanced our ability to dissect CRC mechanisms, identify therapeutic vulnerabilities, and evaluate drug responses in a physiologically relevant context. However, current models still face critical limitations, such as the lack of systemic physiology (e.g., gut–liver or gut–brain axes), limited standardization across platforms, and the need for large-scale, prospective clinical validation. These gaps highlight an urgent need for next-generation platforms and computational frameworks. The development of high-throughput multi-omics, CRISPR-based perturbation, drug screening technologies, and artificial intelligence-driven predictive approaches will offer a promising avenue to address these challenges, accelerating mechanistic studies of CRC, enabling personalized therapy, and facilitating clinical translation. In this perspective, we propose a roadmap for CRC organoid research centered on two major technical pillars: advanced organoid platforms, including immune co-culture and mini-colon systems, and mechanistic investigations leveraging multi-omics and CRISPR-based functional genomics. We then discuss translational applications, such as high-throughput drug screening, and highlight emerging computational and translational strategies that may support future clinical validation and precision medicine. Full article

Atrial fibrillation (AF) is the most common sustained heart rhythm disorder. It is estimated that AF affects over 52 million people worldwide, with its prevalence expected to double in the next four decades. AF significantly increases the risk of stroke and heart failure, contributing to 340,000 excess deaths annually. Beyond these life-threatening complications, AF results in limitations in physical, emotional, and social well-being causing significant reductions in quality of life and resulting in 8.4 million disability-adjusted life-years per year, highlighting the wide-ranging impact of AF on public health. Moreover, AF is increasingly recognized for its association with cognitive decline and dementia. AF is a chronic and progressive disease characterized by rapid and erratic electrical activity in the atria, often in association with structural changes in the heart tissue. AF is often initiated by triggered activity, often from ectopic foci in the pulmonary veins. These triggered impulses may initiate AF via: (1) sustained rapid firing with secondary disorganization into fibrillatory waves, or (2) by triggering micro re-entrant circuits around the pulmonary venous-LA junction and within the atrial body. In each instance, AF perpetuation necessitates the presence of a vulnerable atrial substrate, which perpetuates and stabilizes re-entrant circuits through a combination of slowed and heterogeneous conduction, as well as functional conduction abnormalities (e.g., fibrosis disrupting tissue integrity, and abnormalities in the intercalated disks disrupting effective cell-to-cell coupling). The re-entry wavelength, determined by conduction velocity and refractory period, is shortened by slowed conduction, favoring AF maintenance. One major factor contributing to these changes is the disruption of the extracellular matrix (ECM), which is induced by atrial fibrosis. Fibrosis-driven disruption of the ECM, especially in the heart and blood vessels, is commonly caused by conditions such as aging, hypertension, diabetes, smoking, and chronic inflammatory or autoimmune diseases. These factors lead to excessive collagen and protein deposition by activated fibroblasts (i.e., myofibroblasts), resulting in increased tissue stiffness, maladaptive remodeling, and impaired organ function. Fibrosis typically occurs when cardiac fibroblasts are activated to myofibroblasts, resulting in the deposition of excessive collagen and other proteins. This change in ECM interferes with the normal electrical function of the heart by creating irregular, fibrotic regions. AF and atrial fibrosis have a reciprocal relationship: AF promotes fibrosis through fibroblast activation and extracellular matrix buildup, while atrial fibrosis can sustain and perpetuate AF, contributing to higher rates of AF recurrence after treatments such as catheter ablation or cardioversion. Full article

Ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality despite advances in prevention, diagnosis, and therapy. Traditional clinical risk scores and biomarkers often fail to fully capture the complex molecular processes underlying atherosclerosis, myocardial infarction, and ischemic cardiomyopathy, leaving substantial residual risk. MicroRNAs have emerged as promising regulators and biomarkers of cardiovascular disease, among which microRNA-21 (miR-21) has attracted particular attention. MiR-21 is deeply involved in key pathophysiological mechanisms of IHD, including endothelial dysfunction, vascular inflammation, vascular smooth muscle cell proliferation, plaque development and vulnerability, cardiomyocyte survival, and myocardial fibrosis. Accumulating clinical evidence suggests that circulating miR-21 holds diagnostic value across the ischemic continuum, from stable coronary artery disease to acute coronary syndromes, myocardial infarction, and ischemic heart failure. Moreover, miR-21 demonstrates prognostic relevance, correlating with plaque instability, adverse remodeling, heart failure progression, and long-term cardiovascular outcomes. Preclinical studies further indicate that miR-21 represents a double-edged therapeutic target, offering cardio protection in acute ischemic injury while contributing to fibrosis and maladaptive remodeling if dysregulated. This narrative review summarizes current evidence on the diagnostic, prognostic, and therapeutic utility of miR-21 in IHD, highlighting its clinical promise as well as key limitations and future translational challenges. Full article

Host–virus relationships regulate every phase of viral infection and critically influence course of illness and the effectiveness of treatment. Viruses utilize host receptors, intracellular trafficking routes, metabolic programs, and immunological signaling networks to introduce infection, while host cells use innate and adaptive immune responses that both limit viral replication and, in certain situations, cause tissue damage. Given the fast viral evolution and drug resistance linked to virus-directed therapy, there is growing proof that these host-dependent mechanisms are appealing and underutilized targets for antiviral treatment. Recent developments in single-cell technology, proteomics, and functional genomics have made it possible to systematically identify host dependency and restriction factors shared by different viral families, exposing common molecular vulnerabilities that might be targeted therapeutically. This review integrates current knowledge of virus–host interplay via a translational lens, highlighting processes that directly guide the formation of host-directed antivirals and immune-regulating treatments. We emphasize host processes involved in viral entry, replication, and immune signaling that have shown therapeutic significance, while illustrating the difficulties of balancing antiviral effectiveness with immunopathology. By framing host–virus interactions through a therapeutic lens, this review attempts to offer a targeted and translationally relevant viewpoint for next-generation antiviral research. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

The ubiquity of smartphones has transformed them into primary repositories of sensitive data; however, traditional one-time authentication mechanisms create a critical trust gap by failing to verify identity post-unlock. Our aim is to mitigate these vulnerabilities and align with the Zero Trust Architecture (ZTA) framework and philosophy of “never trust, always verify,” as formally defined by the National Institute of Standards and Technology (NIST) in Special Publication 800-207. This study introduces a robust continuous authentication (CA) framework leveraging multimodal behavioral biometrics. A dedicated application was developed to synchronously capture touch, sliding, and inertial sensor telemetry. For feature modeling, a heterogeneous deep learning pipeline was employed to capture modality-specific characteristics, utilizing Convolutional Neural Networks (CNNs) for sensor data, Long Short-Term Memory (LSTM) networks for curvilinear sliding, and Gated Recurrent Units (GRUs) for discrete touch. To resolve performance degradation caused by class imbalance in Zero Trust environments, a Grid Search Optimization (GSO) strategy was applied to optimize a weighted voting ensemble, identifying the global optimum for decision thresholds and modality weights. Empirical validation on a dataset of 35,519 samples from 15 subjects demonstrates that the optimized ensemble achieves a peak accuracy of 99.23%. Sensor kinematics emerged as the primary biometric signature, followed by touch and sliding features. This framework enables high-precision, non-intrusive continuous verification, bridging the critical security gap in contemporary mobile architectures. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

Climate change has exacerbated flood risks for urban infrastructure, rendering sewage treatment facilities (STFs) particularly vulnerable due to their typical low-lying topographic placement. However, conventional flood risk assessment methodologies often rely solely on physical hazard parameters such as inundation depth, neglecting the functional interdependencies and operational criticality of individual treatment units. To address this limitation, this study proposes the Integrated Hydro-Operational Risk Assessment (IHORA) framework. The IHORA framework synthesizes 2D hydrodynamic modeling with a modified Hazard and Operability Study(HAZOP) study to systematically identify unit-specific physical failure thresholds and employs the Analytic Hierarchy Process (AHP) to quantify the relative operational importance of each process based on expert elicitation. The framework was applied to an underground STF under both fluvial flooding and internal structural breach scenarios. The results revealed a significant risk misalignment in traditional assessments; vital assets like electrical facilities were identified as high-risk hotspots despite moderate physical exposure, due to their high operational weight. Furthermore, Cause–Consequence Analysis (CCA) was utilized to trace cascading failure modes, bridging the gap between static risk metrics and dynamic emergency response protocols. This study demonstrates that the IHORA framework provides a robust scientific basis for prioritizing mitigation resources and enhancing the operational resilience of environmental facilities. Full article