Search Results (55,989)

Background: Migraine is a highly prevalent neurological disorder associated with substantial disability and socioeconomic burden. Although pharmacological therapies remain the mainstay of treatment, their effectiveness may be limited by incomplete response and adverse effects. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a non-invasive neuromodulatory technique that may modulate cortical excitability and pain-processing networks involved in migraine pathophysiology. This systematic review aimed to evaluate the current evidence regarding the efficacy and safety of rTMS compared with sham stimulation in individuals with migraine. Methods: A systematic search was conducted in PubMed (MEDLINE), PsycNet, and Ovid (including MEDLINE and Embase) from database inception to December 2025 in accordance with PRISMA 2020 guidelines. Studies investigating rTMS in adults with migraine and including a sham comparator were eligible for inclusion. Data regarding study design, participant characteristics, rTMS parameters, outcomes, and adverse events were extracted using a predefined template. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Results: Seven studies comprising a total of 301 participants were included. Most trials evaluated high-frequency rTMS targeting the dorsolateral prefrontal cortex. Across studies, rTMS was generally associated with reductions in migraine frequency and severity compared with sham stimulation, although results varied depending on stimulation parameters and study design. Treatment was consistently well tolerated, with only mild and transient adverse effects reported. However, considerable heterogeneity was observed in diagnostic criteria, stimulation protocols, outcome measures, and follow-up duration. Conclusions: Preliminary evidence suggests that rTMS may represent a promising and well-tolerated neuromodulatory approach for migraine management. Nevertheless, methodological variability, limited sample sizes, and concerns regarding risk of bias restrict definitive conclusions. Larger randomized controlled trials with standardized protocols and longer follow-up periods are needed to clarify the clinical role of rTMS in migraine treatment. Full article

Unmanned aerial vehicle (UAV) imaging systems are increasingly deployed in surveillance, infrastructure monitoring, and smart-city applications, where the integrity of captured visual data is critical. Recent advances in generative models enable highly realistic deepfake manipulations that can compromise aerial sensor streams, particularly under real-world degradations such as motion blur, sensor noise, and compression artifacts. This paper introduces DeepLayer-ID, a degradation-aware multi-domain forensic framework specifically designed for UAV sensing environments. The proposed architecture decomposes forensic evidence into complementary spatial, frequency, and residual domains. A discrete wavelet transform module captures sub-band energy inconsistencies, while high-pass residual filtering isolates sensor pattern anomalies. A lightweight transformer-based fusion mechanism adaptively integrates cross-domain representations to enhance robustness under heterogeneous acquisition conditions. To emulate operational UAV pipelines, we construct a balanced dataset of 1096 aerial frames derived from the VisDrone2019-DET validation subset, incorporating synthetic manipulations and physics-consistent degradations. The experimental results show that DeepLayer-ID achieves 97.8% accuracy and 0.991 AUC, outperforming ResNet-50 (90.9%, 0.942 AUC), XceptionNet (92.4%, 0.957 AUC), and Noiseprint CNN (93.1%, 0.964 AUC). Notably, the model maintains real-time feasibility, with only 5.4 M parameters and 9.8 ms inference latency. These findings demonstrate that structured multi-domain signal decomposition combined with attention-guided fusion provides a robust and computationally efficient solution for deepfake detection in degraded UAV sensing systems. Full article

Effective operation and maintenance (O&M) of deep-sea observation networks are challenged by complex environments and energy limitations. While digital twin (DT) technology offers promising solutions, existing frameworks struggle with high-fidelity, multi-platform orchestration and predictions of electrical energy state. This study proposes a DT framework for a deep-sea observation network (DSON-DT), encompassing telemetry acquisition, predictive analytics, and feedback control to realize a closed-loop workflow for monitoring and managing platform states within virtual scenes. Powered by real-time Internet of underwater things (IoUT) data, a high-fidelity virtual environment is constructed in the Unreal Engine 5 game engine, accurately mapping ambient marine environments and reconstructing platform dynamic behaviors via data-driven approaches and geometric constraints. An improved auto-regressive long short-term memory (AR-LSTM) network is proposed to forecast the battery state of charge (SoC). Experimental results show that this algorithm effectively mitigates the impacts of severe deep-sea noise and the flat open-circuit voltage plateau, suppressing state oscillations to provide reliable references for proactive endurance management. The Vue.js-based web prototype, deployed via pixel streaming, offers seamless interfaces for interactive visualization, analysis, and remote operation. This research achieves comprehensive situational awareness for deep-sea platforms, providing validated technical support for the holistic evaluation and intelligent O&M of heterogeneous marine infrastructures. Full article

Background: Sézary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8⁺ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n = 26) and PCAECTCL (n = 13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Fisher’s exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. A conversational AI agent, AI-HOPE, was used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics. Full article

Ground-penetrating radar (GPR) has applications across many domains, including archaeology, mining, and infrastructure inspection. This review is specifically focused on urban subsurface utility mapping, where accurate detection of buried pipelines, cables, and conduits is critical for excavation safety and infrastructure management. Within this scope, two major barriers are identified: event–utility mismatch and the synthetic–field domain gap. Bibliometric analysis shows increasing reliance on deep learning, yet most methods remain limited to event-level hyperbola detection rather than utility-level inference. In real urban environments, radar responses are often affected by orientation-dependent signatures, clutter, overlapping reflections, and non-utility anomalies, making detected events difficult to map directly to physical infrastructure. In parallel, models trained on synthetic data frequently show limited field generalization because simulated radargrams do not fully reproduce soil heterogeneity, acquisition variability, and system artifacts. The review argues that future progress in urban utility mapping requires a shift toward utility-level reasoning supported by multi-sensor fusion, physics-guided learning, hybrid simulation–field datasets, and uncertainty-aware interpretation. Such advances are essential for making GPR outputs more reliable and actionable in urban engineering practice. Full article

Gravel particles are widely developed and randomly distributed in deep reservoirs of the Tarim Oilfield, western China. The mechanical behavior of conglomerate, the main component of the gravel layer, under varying confining pressure and different gravel content, remains poorly understood, especially in terms of the microscopic aspect, which limits the analysis of the variation patterns of underground engineering parameters. This study conducts triaxial compression tests on outcrop specimens from various stress levels to analyze the effects of stress state and stress differences on the mechanical parameters and failure modes. After that, a kind of numerical modeling method based on the discrete element method (DEM) is proposed, which considers the random distribution of gravel particles, to study the microscopic observation of mechanical characteristics and crack propagation of conglomerate under different stress state conditions. The experimental and numerical simulation results indicate that the horizontal strain before failure remains nearly constant in the axial direction while increasing linearly for the horizontal stress. And, it was observed that the volumetric failure was accompanied by gravel fragmentation, sliding, and falling. Numerical simulations reveal that cementation strength and gravel content significantly influence mechanical properties and failure modes, which are the main factors. This study provides some useful references for further understanding of the mechanical behavior and failure mechanisms of rocks in the gravel layer, in particular, the numerical modeling method for heterogeneous materials. Full article

Background/Objectives: Fungal peritonitis is a severe complication of peritoneal dialysis (PD) associated with catheter removal, technique failure, and increased mortality. Although Candida albicans was traditionally the predominant pathogen, non-albicans Candida (NAC) species are increasingly reported. This review summarizes the epidemiology and outcomes of PD-associated NAC peritonitis. Methods: A systematic review was performed following PRISMA guidelines. PubMed/MEDLINE, Scopus, and Google Scholar were searched (January 1990–March 2026) for NAC peritonitis studies. Case reports and series with species-level identification were included. Results: 31 studies met the inclusion criteria, comprising 25 individual case reports and 6 case series, totaling 89 NAC isolates. Candida parapsilosis was the most frequently reported species (n = 50), followed by Candida tropicalis (n = 15). Other pathogens included Candida glabrata, Candida guilliermondii, and several rare NAC species. Fluconazole was the most commonly used initial antifungal therapy. Catheter removal was performed in most cases, with the majority of patients requiring transition to hemodialysis. Overall mortality was 20% among individual case reports vs. 24% across case series. Species-specific differences were observed: C. parapsilosis and C. guilliermondii were generally associated with favorable outcomes, whereas infections involving C. glabrata and other emerging NAC species more frequently required treatment escalation and were linked to poorer outcomes. Conclusions: NAC species are an important cause of fungal peritonitis in PD patients and show considerable heterogeneity in clinical outcomes and antifungal susceptibility. Early species-level identification and prompt catheter removal remain essential for optimal management. Full article

Background/Objectives: Malignant PEComa is a rare sarcoma subtype and usually represents PEComa-NOS (not otherwise specified), one of the several entities of the PEComa family. Surgery is the primary treatment for localized disease; chemotherapy is used mainly for metastatic or unresectable cases. Radiotherapy (RT) may be considered in selected cases; however, its role remains unclear due to the rarity of the disease and limited radiotherapy-specific studies. Methods: This is a descriptive literature review of a limited number of reports on RT use in PEComa. Descriptive statistics were used to summarize reported case characteristics and outcomes. Results: We identified 28 publications reporting 33 cases. In neoadjuvant settings, there was a significant local response to RT in one case. In other neoadjuvant cases, although quantitative response assessments were not reported, most showed no recurrence during follow-up, with the longest follow-up at 34 months, suggesting that a possible benefit in local disease control may exist. In the adjuvant setting, some reports described prolonged disease-free survival following RT, though the lack of direct comparisons between surgery with versus without RT and heterogeneous follow-up periods limit definitive conclusions. In selected metastatic cases, palliative RT achieved notable local responses, potentially contributing to durable local control. Conclusions: In conclusion, although the only available data on RT in PEComas come from case studies with overall heterogeneous management approaches, RT has shown some potential as a therapeutic option across neoadjuvant, adjuvant, and palliative settings, warranting further dedicated clinical studies. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

The mountainous southwest of Saudi Arabia exhibits complex, highly seasonal precipitation driven by Indian Ocean monsoon inflows and orographic lifting. To characterize storm hyetographs, cluster analysis was applied to 8972 rainfall events recorded at 151 gauges. Two primary clusters emerged: one with early, intense peaks and another with later peak intensities, broadly reflecting windward versus leeward storm behavior. A locally derived hyetograph profile (AI) was constructed from the cluster centroids and benchmarked against standard design-storm distributions (Uniform, SCS Type II, Huff quartiles). Across fit metrics—cumulative RMSE, Kolmogorov–Smirnov distance, and cosine-intensity similarity—the AI distribution provided the best match for ~46% of storms, markedly outperforming canonical profiles (Uniform and SCS Type II each best-fit only ~11–12%). These results indicate that region-specific rainfall distributions more accurately represent precipitation patterns than conventional profiles, and that tailored hyetographs can improve hydrologic modeling and water-resources assessments in this climatically heterogeneous region. Full article

Automatic Identification System (AIS) trajectories provide valuable spatiotemporal information for maritime route structure mining, but robust extraction of key route nodes remains difficult because raw data are noisy, turning behaviors are easily masked by local fluctuations, and conventional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is sensitive to fixed parameters and ignores heading differences. To address these issues, this study proposes a key route node extraction framework based on multi-constraint turning-point identification and heading-aware adaptive DBSCAN (HA-DBSCAN). Raw AIS data are first cleaned, segmented, and compressed using a heading-aware Douglas–Peucker strategy to reduce redundancy while preserving geometric and directional characteristics. Valid turning points are then identified by jointly considering heading change rate, geometric curvature, and temporal stability. Finally, HA-DBSCAN integrates a heading-aware distance metric, adaptive neighborhood estimation, and density-aware MinPts optimization to cluster turning points and extract representative route nodes. Experiments using AIS data from the Ningbo–Zhoushan Port area retained 287,614 valid records and 754 continuous trajectory segments, from which 1710 turning points were identified. The proposed method generated 45 stable clusters with a noise ratio of 0.0450 and route coverage of 95.5%. These results indicate that, within the current study setting, the framework can distinguish crossing routes, adapt to heterogeneous traffic densities, and provide an interpretable intermediate layer for subsequent maritime route-structure modeling. Supplementary validation on the same AIS dataset further showed that, compared with DBSCAN, Ordering Points To Identify the Clustering Structure (OPTICS), and HDBSCAN baselines as well as several pipeline ablations, the full framework achieved a more balanced performance in terms of coverage, noise suppression, and avoidance of cluster over-fragmentation. Full article

The secondary immunomodulatory effects of conventional therapeutics, such as antibiotics and cytostatics, are frequently overlooked despite their significant clinical implications. Building on our previous findings that drugs like paclitaxel and doxorubicin heavily influence macrophage polarization—potentially driving metastasis or inflammation—this study systematically evaluates the secondary immune-modulating actions of standard drugs and natural adjuvants. Using patient-derived bronchoalveolar lavage (BAL) fluid (ex vivo alveolar macrophages), we developed an analytical platform using synthetic carbohydrate-functionalized fluorescent ligands targeting key receptors (CD206, CD209, CD280, CD301). Integrating ligand-binding profiles with Linear Discriminant Analysis (LDA) yielded quantitative immune-state vectors capable of differentiating favorable and unfavorable prognostic signatures and imbalanced immune states. Pro-filing samples across heterogeneous respiratory conditions revealed highly con-text-dependent responses. While some treatments synergistically corrected unfavorable imbalanced profiles, others provoked dysregulation. Notably, in pneumonia or bronchitis with an asthma-prone M2-dominant profile, specific antibiotic regimens are critical; doxycycline, for instance, may exacerbate patient deterioration by further driving M2a polarization. Crucially, we identified that natural adjuvants (e.g., curcumin, coumarins, polyphenols) exhibit potent properties capable of correcting these adverse secondary drug effects. Ultimately, this profiling platform highlights the necessity of evaluating patient-specific secondary drug effects, offering a functional blueprint for precision immunotherapy and the rational design of adjuvant-enhanced treatments. Full article

The primary aim of this study is to identify the driving mechanisms behind long-term water-level changes and drought–flood transitions in Dongting Lake. To achieve this, we employed methods including the Improved Standardized Water Level Index (ISWI), Mann–Kendall test, Sen’s slope estimator, and a random forest–SHAP model to analyze hydro-meteorological data from 1992 to 2023. The results demonstrate a significant overall decline and spatial heterogeneity in water levels, alongside a systemic shift in the regional pattern from flood-dominated conditions to frequent droughts with intense drought–flood abrupt alternations. Crucially, during the critical autumn water recession period, runoff anomalies from the Yangtze River’s three outlets emerged as the dominant factor driving water-level changes, far exceeding the influence of local precipitation. Furthermore, a recent downward shift in the water level–discharge relationship indicates that under identical inflow conditions, water levels are now 1.5 to 2.0 m lower than in previous decades. These general findings highlight that critical-period inflow reductions and altered boundary hydrodynamic conditions mutually amplify low-water-level risks, providing a scientific reference for adaptive water resource management in complex river-connected lakes. Full article

Idiopathic inflammatory myopathies constitute a group of immune-mediated disorders primarily affecting skeletal muscle, but they may also lead to significant involvement of internal organs. These conditions are highly heterogeneous, encompassing diverse clinical manifestations and multiple underlying pathophysiological mechanisms. A unifying feature across this disease spectrum is an autoimmune response characterized by the production of highly specific autoantibodies, which are detected in the majority of patients. Genetic studies have identified the principal susceptibility background as the 8.1 ancestral haplotype within the HLA region on chromosome 6. However, genetic predisposition extends beyond HLA loci and includes numerous genes encoding key molecules involved in cytokine production, the regulation of immune signaling pathways, and metabolic processes. In this paper, we review the currently identified genetic loci associated with inflammatory myopathies, with particular emphasis on the HLA system, as well as non-HLA genes and newly identified candidates. Full article

Background: Sepsis has heterogeneous etiologies. Although bacteria are the most common causative agents, viral and yeast forms of sepsis also occur. Procalcitonin (PCT) is widely used to monitor severe bacterial infections and may support the differential diagnosis of infection etiology. Methods: We evaluated the diagnostic value of PCT in viral sepsis using PCT levels at ICU admission and PCT kinetics during ICU treatment. Results: During the COVID-19 pandemic, 191 adult ICU patients with sepsis and a positive SARS-CoV-2 PCR test at hospital admission were included and classified into two groups according to the presence or absence of bacterial or yeast co-infection. PCT showed a distinct diagnostic pattern influenced by the presence of co-infection. PCT remained low in isolated viral sepsis, whereas elevated concentrations were associated with superimposed bacterial or yeast co-infection and worse clinical outcomes. Overall mortality was significantly lower in patients with isolated viral sepsis compared to those with co-infection (50 vs. 69%, p = 0.009). Conclusions: In viral sepsis, persistently low PCT concentrations argue against bacterial co-infection, whereas elevated or rising values should prompt increased diagnostic evaluation. Although PCT provides clinically relevant diagnostic information, it must be interpreted cautiously and in conjunction with clinical assessment and microbiological data. PCT should serve as an adjunctive, not a standalone, marker of infection etiology in sepsis. Full article