Search Results (62)

Acute heart failure (AHF) is a clinical syndrome characterized by the sudden onset or rapid worsening of heart failure signs and symptoms, frequently triggered by myocardial ischemia, pressure overload, or cardiotoxic injury. A central component of its pathophysiology is the activation of intracellular signal transduction cascades that translate extracellular stress into cellular responses. Among these, the mitogen-activated protein kinase (MAPK) pathways have received considerable attention due to their roles in mediating inflammation, apoptosis, hypertrophy, and adverse cardiac remodeling. The canonical MAPK cascades—including extracellular signal-regulated kinases (ERK1/2), p38 MAPK, and c-Jun N-terminal kinases (JNK)—are activated by upstream stimuli such as angiotensin II (Ang II), aldosterone, endothelin-1 (ET-1), and sustained catecholamine release. Additionally, emerging evidence highlights the role of receptor-mediated signaling, cellular stress, and myeloid cell-driven coagulation events in linking MAPK activation to fibrotic remodeling following myocardial infarction. The phosphatidylinositol 3-kinase (PI3K)/Akt signaling cascade plays a central role in regulating cardiomyocyte survival, hypertrophy, energy metabolism, and inflammation. Activation of the PI3K/Akt pathway has been shown to confer cardioprotective effects by enhancing anti-apoptotic and pro-survival signaling; however, aberrant or sustained activation may contribute to maladaptive remodeling and progressive cardiac dysfunction. In the context of AHF, understanding the dual role of this pathway is crucial, as it functions both as a marker of compensatory adaptation and as a potential therapeutic target. Recent reviews and preclinical studies have linked PI3K/Akt activation with reduced myocardial apoptosis and attenuation of pro-inflammatory cascades that exacerbate heart failure. Among the multiple signaling pathways involved, glycogen synthase kinase-3β (GSK-3β) has emerged as a key regulator of apoptosis, inflammation, metabolic homeostasis, and cardiac remodeling. Recent studies underscore its dual function as both a negative regulator of pathological hypertrophy and a modulator of cell survival, making it a compelling therapeutic candidate in acute cardiac settings. While earlier investigations focused primarily on chronic heart failure and long-term remodeling, growing evidence now supports a critical role for GSK-3β dysregulation in acute myocardial stress and injury. This comprehensive review discusses recent advances in our understanding of the MAPK signaling pathway, the PI3K/Akt cascade, and GSK-3β activity in AHF, with a particular emphasis on mechanistic insights, preclinical models, and emerging therapeutic targets. Full article

Earthquakes are sudden-onset disasters requiring rapid, accurate information for effective crisis response. Social media (SM) platforms provide abundant geospatial data but are often unstructured and produced by diverse users, posing challenges in filtering relevant content. Traditional content filtering methods rely on natural language processing (NLP), which underperforms with mixed-language posts or less widely spoken languages. Moreover, these approaches often neglect the spatial proximity of users to the event, a crucial factor in determining relevance during disasters. This study proposes an NLP-free model that assesses the spatial credibility of SM content by analysing users’ spatial trajectories. Using earthquake-related tweets, we developed a machine learning-based classification model that categorises posts as directly relevant, indirectly relevant, or irrelevant. The Random Forest model achieved the highest overall classification accuracy of 89%, while the k-NN model performed best for detecting directly relevant content, with an accuracy of 63%. Although promising overall, the classification accuracy for the directly relevant category indicates room for improvement. Our findings highlight the value of spatial analysis in enhancing the reliability of SM data (SMD) during crisis events. By bypassing textual analysis, this framework supports relevance classification based solely on geospatial behaviour, offering a novel method for evaluating content trustworthiness. This spatial approach can complement existing crisis informatics tools and be extended to other disaster types and event-based applications. Full article

Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems. Full article

Landslides are among the most frequent geological hazards, often resulting in casualties and economic losses, particularly in alpine valley areas characterized by complex topography and dense vegetation. Landslides in these regions are distinguished by their high altitude, concealment, and sudden onset, which render traditional monitoring methods inefficient. This study proposes a landslide monitoring method for complex environments that leverages multi-source remote sensing data, incorporating the radiative transfer model and Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. The proposed method was implemented to monitor the instability of the Baige landslide in Tibet, China. The results show that the vegetation Canopy Water Content (CWC) estimated using the radiative transfer model indirectly reflects landslide susceptibility. Specifically, excessive soil moisture from rainfall reduces oxygen in plant roots, affecting growth and lowering canopy water content. The region with lower Canopy Water Content (CWC < 0.04) exhibited an increasing trend in the number of pixels, rising from 271 to 549 before the landslide event, indicating poorer vegetation conditions in the area. Additionally, the SBAS-InSAR technique was utilized to extract surface displacement, achieving a maximum displacement of 112 mm during the monitoring period. Ultimately, the spatial changes of the two monitoring signals exhibited a high consistency. This study enhances the reliability of landslide displacement monitoring in complex environments and provides substantial scientific support for future large-scale monitoring efforts. Full article

Background and Objectives: Aortic stenosis (AS) is a frequent valvular disease characterized by the obstruction of left ventricular outflow. The resulting hemodynamic and structural changes create an arrhythmogenic substrate, with sudden cardiac death (SCD) often caused by ventricular arrhythmias (VAs) being a feared complication. This review examines the relationship between severe AS and VA, detailing the epidemiology, pathophysiological mechanisms, risk factors, and management approaches prior to aortic valve replacement (AVR). Materials and Methods: We conducted a comprehensive narrative review of the historical and contemporary literature investigating ventricular arrhythmias in severe aortic stenosis. Literature searches were performed in PubMed, MEDLINE, and Scopus databases using keywords, including “aortic stenosis”, “ventricular arrhythmia”, “sudden cardiac death”, and “aortic valve replacement”. Both landmark historical studies and modern investigations utilizing advanced monitoring techniques were included to provide a complete evolution of the understanding. Results: The prevalence of ventricular ectopy and non-sustained ventricular tachycardia increases with AS severity and symptom onset. Left ventricular hypertrophy, myocardial fibrosis, altered electrophysiological properties, and ischemia create the arrhythmogenic substrate. Risk factors include the male sex, concomitant aortic regurgitation, elevated filling pressures, and syncope. Diagnostic approaches range from standard electrocardiography to continuous monitoring and advanced imaging. Management centers on timely valve intervention, with medical therapy serving primarily as a bridge to AVR. Conclusions: Ventricular arrhythmias represent a consequence of valvular pathology in severe AS rather than an independent entity. Their presence signals advanced disease and a heightened risk for adverse outcomes. Multidisciplinary management with vigilant monitoring and prompt surgical referral is essential. Understanding this relationship enables clinicians to better identify high-risk patients requiring urgent intervention before life-threatening arrhythmic events occur. Full article

Objective: Sudden Death (SD) is a high-mortality emergency event that typically occurs within one hour of symptom onset. Accurate risk prediction is essential for optimizing post-resuscitation care. This study aims to enhance the survival rate of patients experiencing sudden death by developing and validating a risk prediction model for in-hospital mortality following successful resuscitation. Method: This study is a retrospective analysis of data that were collected prospectively from a standardized clinical database. All data were recorded at the time of patient admission using a predefined protocol to ensure consistency and accuracy. We retrospectively analyzed the data collected from 295 patients who experienced sudden death and achieved successful resuscitation at Wuhan Central Hospital from January 2017 to June 2024. The patients were assigned to groups using a randomization process into training and validation sets using k-fold cross-validation and further categorized within these sets based on in-hospital mortality as the outcome. A prediction model was constructed, and its efficacy was validated using logistic regression analysis, which was visualized with nomograms. Results: The results of this regression analysis of the training set demonstrated the actual length of hospital stay, in-hospital norepinephrine dosage, post-resuscitation respiratory rate, and sinus rhythm after resuscitation as independent influencing factors (p < 0.05), which formed the basis of the prediction model. The analysis of the training set exhibited high discriminative ability, with an area under the ROC curve (AUC) of 0.860, which exceeds the commonly accepted threshold for good classification performance, and the calibration, applicability, and reasonableness were all favorable. When the model was applied to the validation set, the AUC was 0.758, and the discrimination, calibration, applicability, and reasonableness of the validation set were also satisfactory. Conclusions: the main conclusion is that a risk prediction model for in-hospital mortality following resuscitation from sudden death was successfully developed and internally validated, offering a significant advancement in clinical decision-making support. Full article

In recent years, there have been frequent extreme weather events that defy traditional understanding. Specifically, mountain flood disasters can cause significant loss of life due to their sudden onset and destructive power. The 7.21 flood event in Xingyang, Zhengzhou, China, recorded a maximum 6 h precipitation of 240.5 mm in the Suo River basin, corresponding to a 500-year return period, and causing fatalities and substantial damage. The central government of China has launched supplementary mountain flood disaster surveys and evaluations involving key towns and villages, following an initial round of surveys in riverside villages, to improve foresight and response capabilities for mountain flood disaster risks under extreme conditions. This paper introduces the contents of the national mountain flood disaster surveys and evaluations of key towns and villages, elaborating on the principles, content, and rules for auditing the national survey and evaluation results. This paper innovatively proposes professional audit criteria, such as early warning indicators, monitoring facility correlations, and hazard zoning, based on a formal audit of the data quality. The implementation of professional audit criteria improved the data accuracy by 85% and reduced false alarms by 40%, enhancing the overall effectiveness of mountain flood disaster prevention. The analysis of the audit results suggests that the audit rules for the survey and evaluation results of key towns are scientific, reasonable, and effective, achieving the expected goals of data quality control. This approach can effectively enhance the practical value of the survey and evaluation outcomes for key towns, laying a solid data foundation for transforming flood disaster prevention from merely “existing” to “optimal”. Full article

On 10 May 2024 at 17 h:07 UTC, the simultaneous arrival of several solar coronal mass ejections (CMEs) generated the strongest geomagnetic storm of the last twenty years, with a minimum Dst = −412 nT, usually referred to as the Mother’s Day event. On 10 October 2024, the second strongest event of solar cycle 25 appeared with a Dst = −335 nT, preceded on 8 October by an event with a Dst = −153 nT. In the present work, with measurements of the vertical total electron content and with ionosonde observations from Europe, USA, and South Korea, we show that the ionization of the upper atmosphere shortly increased at the arrival of the CME for these different events, followed by a fast decrease at all latitudes. The ionization remained very low for more than a full day. While the recovery started at the beginning of the second day after the onset for both events in October, the sudden recovery in the middle of the second day on 12 May is much more unusual. The analysis of the observations at different latitudes and longitudes shows that the causes of the ionization variations during the superstorms were mainly due to strong perturbations in the ionospheric F layer, amplified by the plasmasphere’s influence on the vertical total electron content (VTEC). The erosion of the plasmasphere during these two strong events led to a plasmapause located at exceptionally low radial distances smaller than 2 Re (Earth’s radii) in the post-midnight sector and a rotating plume in the afternoon–dusk sector clearly visible in the BSPM plasmasphere model. It took several days after the storms to recover normal ionization rates. Full article

Background: Unilateral basal ganglia calcinosis (BGC) is a rare radiological finding that can be diagnosed on computed tomography (CT) and magnetic resonance imaging (MRI) but often presents challenges for clinicians and radiologists in determining its underlying cause. So far, only a few potential causes that could explain unilateral BGC have been described in the literature. Case Report: A 54-year-old Caucasian male was admitted to a tertiary university hospital due to the sudden onset of speech impairment and right-sided weakness. The patient had no significant medical history prior to this event. Non-enhanced computed tomography (NECT) of the brain revealed no evidence of acute ischemia; CT angiography (CTA) showed acute left middle cerebral artery (MCA) M2 segment occlusion. CT perfusion (CTP) maps revealed an extensive penumbra-like lesion, which is potentially reversible upon achieving successful recanalization. However, a primary neoplastic tumor with calcifications in the basal ganglia was initially interpreted as the potential cause; therefore, acute stroke treatment with intravenous thrombolysis was contraindicated. A follow-up CT examination at 24 h revealed an ischemic lesion localized to the left insula, predominantly involving the left parietal lobe and the superior gyrus of the left temporal lobe. Subsequent gadolinium-enhanced brain MRI revealed small blood vessels draining into the subependymal periventricular veins on the left basal ganglia. Digital subtraction angiography was conducted, confirming the diagnosis of venous angioma. Conclusions: Unilateral BGC caused by venous angioma is a rare entity with unclear pathophysiological mechanisms and heterogeneous clinical presentation. It may mimic conditions such as intracerebral hemorrhage or hemorrhagic brain tumors, complicating acute stroke management, as demonstrated in this case. Surrounding tissue calcification may provide a valuable radiological clue in diagnosing venous angiomas DVAs and vascular malformations. Full article

Background: Cardiac autonomic neuropathy (CAN) is a serious but sometimes underdiagnosed complications of Diabetes Mellitus (DM). Because of the subtle onset and non-specific symptoms that can be mistaken for other conditions, CAN is frequently underdiagnosed despite the serious consequences that can appear. Its significance as an independent risk factor for cardiovascular events, including arrhythmias, sudden cardiac death, and silent myocardial ischemia, is being demonstrated by recent studies. The objective of this review article is to highlight the reasons why CAN is underdiagnosed and its association with decreased cardiovascular risk and promote clinical awareness. This review article summarizes the epidemiology, influence on the cardiovascular system and diagnostic methods of CAN, and the clinical implications of diabetic neuropathy. This review analyzes available data from papers relevant to the topic of diabetic neuropathy, cardiac autonomic neuropathy, and cardiovascular system implications. Conclusions: CAN is still underdiagnosed despite its clinical impact because routine screening is lacking, and healthcare providers are not aware of it. To improve outcomes for people with DM, it is necessary to introduce standardized diagnostic procedures into clinical practice and increase the knowledge about CAN. Full article

The impact of the quasi-biennial oscillation (QBO) and Madden–Julian oscillation (MJO) on the dynamics of major sudden stratospheric warmings (SSWs) observed in the winters of 2018, 2019, and 2021 is investigated. Using data from the MERRA-2 reanalysis, we analyze the daily mean variability of critical atmospheric parameters at the 10 hPa level, including zonal mean polar cap temperature, zonal mean zonal wind, and the amplitudes of planetary waves 1 and 2. The results reveal dramatic increases in polar cap temperature and significant wind reversals during the SSW events, particularly in 2018. The analysis of planetary wave (PW) amplitudes demonstrates intensified wave activity coinciding with the onset of SSWs, underscoring the pivotal role of PWs in these stratospheric disruptions. Further examination of outgoing long-wave radiation (OLR) anomalies highlights the influence of QBO phases on tropical convection patterns. During westerly QBO (w-QBO) phases, enhanced convective activity is observed in the western Pacific, whereas the easterly QBO (e-QBO) phase shifts convection patterns to the maritime continent and central Pacific. This modulation by QBO phases influences the MJO’s role during SSWs, affecting tropical and extra-tropical weather patterns. The day-altitude variability of upward heat flux reveals distinct spatiotemporal patterns, with pronounced warming in the polar regions and mixed heat flux patterns in low latitudes. The differences observed between the SSWs of 2017–2018 and 2018–2019 are likely related to the varying QBO phases, emphasizing the complexity of heat flux dynamics during these events. The northern annular mode (NAM) index analysis shows varied responses to SSWs, with stronger negative anomalies observed during the e-QBO phase compared to the w-QBO phases. This variability highlights the significant role of the QBO in shaping the stratospheric and tropospheric responses to SSWs, impacting surface weather patterns and the persistence of stratospheric anomalies. Overall, the study demonstrates the intricate interactions between stratospheric dynamics, QBO, and MJO during major SSW events, providing insights into the broader implications of these atmospheric phenomena on global weather patterns. Full article

The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases—stop, start, and acceleration—aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk’s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation’s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model’s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation’s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework. Full article

Flash floods are one of the most hazardous natural events globally, characterized by their rapid onset and unpredictability, often overwhelming emergency preparedness and response systems. In the arid environment of Saudi Arabia, Wadi Al-Lith watershed is particularly prone to flash floods, exacerbated by sudden storms and the region’s distinct topographical features. This study focuses on the morphometric characterization and comparative analysis of flash flood risk within the Wadi Al-Lith basin. To assess flood susceptibility, two widely adopted methodologies were employed: the morphometric ranking approach and El-Shamy’s method. A 12.5-m resolution ALOS PALSAR digital elevation model (DEM) was used to delineate the watershed and generate a detailed drainage network via Arc-Hydro tools in the ArcGIS 10.4 software. Fifteen morphometric parameters were analyzed to determine their influence on flood potential and hazard prioritization. The findings of this study provide crucial insights for regional flood risk management, offering an improved understanding of flash flood dynamics and assisting in developing effective mitigation strategies for Wadi Al-Lith and similar environments. The findings reveal that Wadi Al-Lith comprises multiple sub-catchments with varying degrees of vulnerability to flash flooding. According to the morphometric hazard analysis (MHA), certain sub-catchments, including sc-2, sc-4, sc-5, sc-6, sc-10, sc-12, sc-13, and sc-15, emerge as highly susceptible to flood hazards, while others (sc-1 and sc-9) fall into moderate risk categories. In contrast, the application of El-Shamy’s method provides a different ranking of flood risks across the watershed’s sub-catchments, offering a comparative view of flood susceptibility. The insights gained from this dual-analysis approach are expected to support the development of targeted flood prevention and mitigation strategies, which are essential for minimizing the future impacts of flash flooding in the Wadi Al-Lith watershed and ensuring better preparedness for local communities. Full article

An array of SuperDARN meteor radars at northern high latitudes was used to investigate the sources and characteristics of eastward-propagating planetary waves (EPWs) at 95 km, with a focus on wintertime. The nine radars provided the daily mean meridional winds and their anomalies over 180 degrees of longitude, and these anomalies were separated into eastward and westward waves using a fast Fourier transform (FFT) method to extract the planetary wave components of zonal wavenumbers 1 and 2. Years when a sudden stratospheric warming event with an elevated stratopause (ES-SSW) occurred during the winter were contrasted with years without such events and composited through superposed epoch analysis. The results show that EPWs are a ubiquitous—and unexpected—feature of meridional wind variability near 95 km. Present even in non-ES-SSW years, they display a regular annual cycle peaking in January or February, depending on the zonal wavenumber. In years when an ES-SSW occurred, the EPWs were highly variable but enhanced before and after the onset. Full article

Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for preemptive measures aimed at reducing the incidence of such life-threatening events. Hence, this study proposed a predictive algorithm for SCD leveraging single-lead electrocardiogram (ECG) signals featuring low signal-to-noise ratios. Initially, simulated electrode motion artifact noise was introduced to ideal ECG signals to emulate the signal conditions with low signal-to-noise ratios encountered in everyday scenarios. To meet the criteria of simplicity and cost-effectiveness required for wearable devices, the analysis focused exclusively on single-lead signals. The proposed algorithm in this study employed a lightweight machine learning approach to extract 12-dimensional features encompassing ventricular late potentials, T-wave electrical alternation, and corrected QT intervals from the signal. The algorithm achieved an average prediction accuracy of 93.22% within 30 min prior to SCD onset, and 95.43% when utilizing a normal sinus rhythm database as a control, demonstrating robust performance. Additionally, a comprehensive Sudden Cardiac Death Index (SCDI) was devised to quantify the risk of SCD, formulated by integrating pivotal two-dimensional features contributing significantly to the algorithm. This index effectively distinguishes high-risk signals indicative of SCD from normal signals, thereby offering valuable supplementary insights in clinical settings. Full article