Search Results (10,580)

Background/Objectives: Rheumatoid neutrophilic dermatosis (RND) is a rare extra-articular manifestation of rheumatoid arthritis (RA) with variable clinical presentations. Although typically non-blistering, a rare bullous or vesiculobullous subtype has been described, mainly in patients with seropositive and active RA, and may mimic autoimmune blistering diseases. The objective of this review was to systematically summarize the clinical, histopathological, immunopathological, and therapeutic features of vesiculobullous rheumatoid neutrophilic dermatosis. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines utilizing the PubMed, MEDLINE, and Google Scholar databases, which were searched through December 2025. Case reports and case series describing vesiculobullous or bullous RND with extractable patient-level data were included. Non-English articles were translated. Demographic, clinical, histopathological, immunopathological, microbiological, and therapeutic data were extracted and analyzed using Statistica 12.0 software. Results: Results were synthesized descriptively due to clinical heterogeneity and limited sample size. Thirty reported cases were identified, of which 28 non-duplicate cases were included. The mean patient age was 60.8 ± 14.9 years, with a female predominance (male-to-female ratio, 1:2.5). Most patients were of Asian descent (67.9%). Bullous or vesicular lesions most frequently involved the lower legs (64.3%), palms and soles (41.7%), and thighs (35.7%). Rheumatoid factor data were available in 67.9% of patients, all indicating high RA activity. Histopathological examination was reported in 71.4% of cases and most commonly demonstrated a predominantly neutrophilic infiltrate, often dense and extending throughout the dermis, with subepidermal blister formation being the most frequent pattern. Direct immunofluorescence, serological testing for autoimmune bullous diseases, and microbiological investigations were predominantly negative. Dapsone and systemic corticosteroids, alone or combined with RA-specific therapies, were the most commonly used treatments. Conclusions: This review represents the most comprehensive synthesis to date focused exclusively on the bullous/vesiculobullous subtype of RND, highlighting key diagnostic features such as neutrophil-predominant histopathology, negative direct immunofluorescence, and favorable response to dapsone. Full article

Power load forecasting is essential for power system security and energy dispatch. With the increasing renewable integration, load patterns have become more volatile and uncertain, difficult for traditional forecasting methods to maintain high adaptability. To address this challenge, this study proposes the Llama3-QLoRA-GeoWeather framework, a novel power load forecasting approach based on the open-source large language model Llama 3.3 70B. The framework introduces a two-stage progressive fine-tuning strategy based on QLoRA, significantly reducing computational costs and allowing adaptation on constrained hardware. Moreover, geographic features from the OpenStreetMap ecosystem and meteorological data from OpenWeatherMap API are integrated to further enhance the forecasting performance. A comprehensive Llama3-PowerFrame enhancement framework for future power systems is also designed. Experimental results demonstrate that Llama3-QLoRA-GeoWeather achieves the best forecasting performance (MAPE = 1.16%), outperforming the state-of-the-art baselines. This corresponds to a reduction in MAE, RMSE, and MAPE by approximately 42.7%, 67.8%, and 42.3% respectively, providing a viable technical pathway for building the next-generation intelligent load forecasting system across multiple scenarios with high credibility and strong adaptability. Full article

Recent years have witnessed performance improvements in Multimodal Large Language Models (MLLMs) on downstream natural image understanding tasks. However, when applied to the function graph reasoning task, which is highly information-dense and abundant in fine-grained structural details, these models face pronounced performance degradation. The challenges are primarily characterized by several core issues: the static projection bottleneck, inadequate cross-modal interaction, and insufficient visual context in text embeddings. To address these problems, this study proposes a multimodal feature fusion enhancement method for function graph reasoning and constructs the FuncFusion-Math model. The core innovation of this model resides in its design of a dual-path feature fusion mechanism for both image and text. Specifically, the image fusion module adopts cross-attention and self-attention mechanisms to optimize visual feature representations under the guidance of textual semantics, effectively mitigating fine-grained information loss. The text fusion module, through feature concatenation and Transformer encoding layers, deeply integrates structured mathematical information from the image into the textual embedding space, significantly reducing semantic deviation. Furthermore, this study utilizes a four-stage progressive training strategy and incorporates the LoRA technique for parameter-efficient optimization. Experimental results demonstrate that the FuncFusion-Math model, with 3B parameters, achieves an accuracy of 43.58% on the FunctionQA subset of the MathVista test set, outperforming a 7B-scale baseline model by 13.15%, which validates the feasibility and effectiveness of the proposed method. Full article

Background: Fetal growth restriction (FGR) is a major contributor to adverse perinatal outcomes and is primarily driven by placental insufficiency and chronic fetal hypoxia. While arterial Doppler abnormalities are widely used in clinical surveillance, less is known about venous hemodynamics and intracardiac structural adaptations in FGR. In particular, the clinical relevance of foramen ovale (FO) morphometry and inferior vena cava (IVC) Doppler parameters in different FGR phenotypes remains incompletely understood. This study aimed to evaluate FO measurements and IVC Doppler indices in early- and late-onset FGR and to investigate their associations with adverse perinatal outcomes. Methods: This prospective observational study included 240 singleton pregnancies: 120 fetuses with FGR and 120 gestational age-matched appropriate-for-gestational-age controls. FGR was defined according to Delphi consensus criteria and classified as early onset (<32 weeks) or late onset (≥32 weeks). Ultrasonographic assessment included FO and right atrium dimensions, FO-to-right atrium (FO/RA) ratio, IVC diameter, and IVC Doppler indices (pulsatility index [PI], preload index [PLI], and peak velocity index for veins [PVIV]). A composite adverse perinatal outcome (CAPO) was recorded. Receiver operating characteristic (ROC) curve analysis and multivariable logistic regression were performed. Results: Compared with controls, fetuses with FGR exhibited significantly smaller FO dimensions, lower FO/RA ratios, reduced IVC diameters, and higher IVC Doppler indices (all p < 0.05). The FO/RA ratio demonstrated the highest discriminative performance for CAPO (AUC 0.722). In multivariable analysis, a 0.1-unit increase in the FO/RA ratio was independently associated with a reduced risk of CAPO (OR 0.57), whereas higher IVC PI values were associated with an increased risk (OR 2.64). IVC Doppler alterations were less pronounced in early-onset FGR. Conclusions: FO morphometry and IVC Doppler parameters reflect complementary stages of fetal cardiovascular adaptation in fetal growth restriction, with FO changes representing early adaptive responses and IVC Doppler alterations indicating more advanced hemodynamic compromise, and this may provide additional value for perinatal risk stratification. Full article

Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework and conducts a systematic empirical study on various LLMs, including GPT-4, Claude-3, Gemini, the Llama series, DeepSeek, and Qwen, comparing them with traditional methods such as ARIMA, BiLSTM, MICN, TimesNet, and VMD-BiLSTM. Furthermore, Ele-LLM, a specialized model based on the Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning strategy, is proposed. Experimental results show that Ele-LLM achieves the best forecasting performance (MAPE = 1.04%), significantly outperforming the best traditional baseline. LLMs also demonstrate notable advantages in few-shot learning, long-sequence dependency modeling, and generalization in complex scenarios. This study provides an evaluation benchmark and practical guidelines for applying LLMs in very short-term power load forecasting, proving their great potential and practical value as an emerging technological pathway. Full article

Extreme summarization generates very short summaries, typically a single sentence, answering the question “What is the document about?”. Although large language models perform well in text generation, fine-tuning them for summarization often requires substantial computational resources that are unavailable to many researchers. In this study, we present an effective method for instruction-tuning open decoder-only large language models under limited GPU resources. The proposed approach combines parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA), with quantization to reduce memory requirements, enabling training on a single consumer-grade GPU. We fine-tuned a pre-trained decoder-only model on the XSum dataset using an instruction-following format. Experimental results demonstrate that the proposed decoder-only approach achieves competitive performance on the XSum dataset under strict GPU memory constraints. On the full test set, the proposed 2G–1R pipeline attains ROUGE-1/2/L F1 scores of 46.0/22.0/37.0 and a BERTScore F1 of 0.917, outperforming the individual generator models in lexical overlap and semantic similarity. Evaluation was conducted using traditional overlap-based metrics (ROUGE) and semantic metrics, including BERTScore and G-Eval. While remaining competitive in ROUGE compared to strong encoder–decoder baselines, the pipeline consistently produces summaries with higher semantic quality. These findings demonstrate that large decoder-only language models can be efficiently fine-tuned for extreme summarization on limited consumer-grade hardware without sacrificing output quality. Full article

Background and Objectives: Patients with type 2 diabetes mellitus (T2DM) and ischemic stroke present with endothelial, vascular and left ventricular (LV) myocardial dysfunction. We investigated the effects of treatment with either glucagon-like peptide-1 receptor agonists (GLP-1RA) or sodium-glucose contrasporter-2 inhibitors (SGLT-2i) on endothelial glycocalyx, arterial stiffness, and LV myocardial strain in patients with metformin-treated T2DM and a prior ischemic stroke. Materials and Methods: A total of 54 consecutive patients with T2DM and ischemic stroke who attended a cardiometabolic outpatient clinic in Athens, Greece, and received either GLP-1RA (dulaglutide; n = 27) or SGLT-2i (empagliflozin; n = 27) were enrolled in the study. We measured the perfused boundary region (PBR) of the sublingual microvessels, a marker of glycocalyx thickness, as well as carotid-femoral pulse wave velocity (PWV) and LV global longitudinal strain (GLS), at baseline and at 4 and 12 months of treatment. Results: Twelve months after treatment, all patients had reduced glycosylated hemoglobin and body mass index (BMI) (p < 0.001). Patients treated with dulaglutide showed a greater reduction in BMI (−11.8% vs. −4.8%, p < 0.001) compared to those treated with empagliflozin. Compared to baseline, all patients had reduced PBR, PWV and GLS (p < 0.001) after 12 months of treatment. However, empagliflozin presented a greater decrease in PWV (−14% vs. −10.9%, p = 0.041), while dulaglutide resulted in a greater increase in GLS (14.7% vs. 8.3%, p = 0.024) compared to empagliflozin. In all patients, the reduction in PBR at 12 months was correlated with a decrease in PWV and with an increase in GLS (p < 0.05). Conclusions: Both dulaglutide and empagliflozin improve cardiovascular function in T2DM patients with ischemic stroke. Dulaglutide appears to be more effective in the improvement of LV myocardial strain, whereas empagliflozin is more effective in reducing arterial stiffness. Full article

Traffic-related air pollution (TRAP) is known to contribute to oxidative stress in the central nervous system (CNS) and has been linked to increased risk of Alzheimer’s disease (AD). Alterations in the renin–angiotensin system (RAS), specifically increased angiotensin II (Ang II) signaling via the angiotensin II type 1 (AT₁) receptor, are implicated in increased oxidative stress in the CNS via activation of NADPH oxidase (NOX). As exposure to TRAP may further elevate AD risk, we investigated whether exposure to inhaled mixed gasoline and diesel vehicle emissions (MVE) promotes RAS-dependent expression of factors that contribute to AD pathophysiology in an apolipoprotein E-deficient (ApoE^−/−) mouse model. Male ApoE^−/− mice (6–8 weeks old) on a high-fat diet were treated with either an ACE inhibitor (captopril, 4 mg/kg/day) or water and exposed to filtered air (FA) or MVE (200 µg PM/m³) for 30 days. MVE exposure elevated plasma Ang II, inflammation, and oxidative stress in the hippocampus, associated with increased levels of Aph-1 homolog B (APH1B), a gamma-secretase subunit, and beta-secretase 1 (BACE1), involved in Aβ production. Each of these endpoints was normalized with ACEi treatment. These findings indicate that TRAP exposure in ApoE^−/− mice drives a RAS- and NOX-dependent oxidative and inflammatory response and shifts Aβ processing towards an amyloidogenic profile before overt Aβ deposition, suggesting a potential therapeutic approach for air pollution-induced AD risk. Full article

The flavivirus NS1 protein is a component of the viral replication complex and plays diverse, yet poorly understood, roles in the viral life cycle. To enable real-time visualization of the developing replication organelle and biochemical analysis of tagged NS1 and its interacting partners, we engineered a replication-competent yellow fever virus (YFV) replicon encoding a C-terminal fusion of NS1 with green fluorescent protein (NS1–GFP). The initial variant was non-viable in the absence of trans-complementation with wild-type NS1; however, viability was partially restored through the introduction of co-adaptive mutations in GFP (Q204R/A206V) and NS4A (M108L). Subsequent cell culture adaptation generated a 17-nucleotide frameshift within the NS1–GFP linker, resulting in a more flexible and less hydrophobic linker sequence. The optimized genome, in the form of a replicon, replicates in packaging cells that produce YFV structural proteins, as well as in naive BHK-21 cells. In the packaging cells, the adapted NS1–GFP replicon produces titers of infectious particles of approximately 10^6 FFU/mL and is genetically stable over five passages. The expressed NS1–GFP fusion protein localizes to the endoplasmic reticulum and co-fractionates with detergent-resistant heavy membranes, a hallmark of flavivirus replication organelles. This NS1–GFP replicon provides a novel platform for studying NS1 functions and can be further adapted for proximity-labeling strategies aimed at identifying the still-unknown protease responsible for NS1–NS2A cleavage. Full article

This study aims to investigate the predictive value of pre-treatment multi-phasic contrast-enhanced computed tomography (CECT) radiomic features for treatment resistance in patients with rat sarcoma virus (RAS)-mutated colorectal liver metastases (CRLMs) receiving bevacizumab-based chemotherapy. Seventy-three samples with RAS-mutated CRLMs receiving bevacizumab-combined chemotherapy regimens were evaluated. Radiomic features were extracted from arterial phase (AP), portal venous phase (PVP), AP-PVP subtraction image, and Delta phase (DeltaP, calculated as AP-to-PVP ratio) images. Three groups of radiomics features were extracted for each phase, including peritumor, core tumor, and whole-tumor regions. For each of the four phases, a two-sided independent Mann–Whitney U test with the Bonferroni correction and K-means clustering was applied to the remnant features for each phase. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied for further feature selection. Six machine learning algorithms were then used for model development and validated on the independent testing cohort. Results showed peritumoral radiomic features and features derived from Laplacian of Gaussian (LoG) filtered images were dominant in all the compared machine learning algorithms; NB models yielded the best-performing prediction (Avg. training AUC: 0.731, Avg. testing AUC: 0.717) when combining all features from different phases of CECT images. This study demonstrates that peritumoral radiomic features and LoG-filtered pre-treatment multi-phasic CECT images were more predictive of treatment response to bevacizumab-based chemotherapy in RAS-mutated CRLMs compared to core tumor features. Full article

To meet 2050 climate targets, the construction sector must reduce CO₂ emissions and transition toward circular material flows. Recycled aggregates (RA) derived from construction and demolition waste (CDW) and industrial byproducts such as oil shale ash (OSA) show potential for use in concrete, although their application remains limited by standardisation and performance limitations, particularly in structural uses. This study aims to develop and evaluate low-strength, resource-efficient concrete mixtures with full replacement of natural aggregates (NA) by CDW-derived aggregates, and partial or full replacement of cement CEM II by OSA–metakaolin (MK) binder, targeting non-structural 3D-printing applications. Mechanical performance, printability, cradle-to-gate life cycle assessment, eco-intensity index, and transport-distance sensitivity for RA were assessed to quantify the trade-offs between structural performance and global warming potential (GWP) reduction. Replacing NA with RA reduced compressive strength by ~11–13% in cement-based mixes, while the aggregate type had a negligible effect in cement-free mixtures. In contrast, full cement replacement by OSA-MK binder nearly halved compressive strength. Despite the strength reductions associated with the use of waste-derived materials, RA-based cement-free 3D-printed specimens achieved ~30 MPa in compression and ~5 MPa in flexure. Replacing CEM II with OSA-MK and NA with RA lowered GWP by up to 48%, with trade-offs in the air-emission, toxicity, water and resource categories driven by the OSA supply chain. The cement-free RA mix achieved the lowest GWP and best eco-intensity, whereas the CEM II mix with RA offered the most balanced multi-impact profile. The results show that regionally available OSA and RA can enable eco-efficient, structurally adequate 3D-printed concrete for construction applications. Full article

Type 2 diabetes mellitus (T2DM) is characterized not only by chronic hyperglycemia but also by profound adipose tissue dysfunction, including impaired lipid handling, low-grade inflammation, mitochondrial dysfunction, and extracellular matrix (ECM) remodeling. These adipose tissue alterations play a central role in the development of systemic insulin resistance, ectopic lipid accumulation, and cardiometabolic complications. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), particularly semaglutide, have emerged as highly effective therapies for T2DM and obesity. While their glucose-lowering and appetite-suppressive effects are well established, accumulating evidence indicates that semaglutide exerts pleiotropic metabolic actions that extend beyond glycemic control, with adipose tissue representing a key target organ. This review synthesizes current preclinical and clinical evidence on the molecular and cellular mechanisms through which semaglutide modulates adipose tissue biology in T2DM. We discuss depot-specific effects on visceral and subcutaneous adipose tissue, regulation of adipocyte lipid metabolism and lipolysis, enhancement of mitochondrial biogenesis and oxidative capacity, induction of beige adipocyte programming, modulation of adipokine and cytokine secretion, immunometabolic remodeling, and attenuation of adipose tissue fibrosis and ECM stiffness. Collectively, available data indicate that semaglutide promotes a functional shift in adipose tissue from a pro-inflammatory, lipid-storing phenotype toward a more oxidative, insulin-sensitive, and metabolically flexible state. These adipose-centered adaptations likely contribute to improvements in systemic insulin sensitivity, reduction in ectopic fat deposition, and attenuation of cardiometabolic risk observed in patients with T2DM. Despite compelling mechanistic insights, much of the current evidence derives from animal models or in vitro systems. Human adipose tissue-focused studies integrating molecular profiling, advanced imaging, and longitudinal clinical data are therefore needed to fully elucidate the extra-glycemic actions of semaglutide and to translate these findings into adipose-targeted therapeutic strategies. Full article

We present the case of an 83-year-old woman with a long-standing history of rheumatoid arthritis (RA) who was found collapsed at home. The patient presented with cardiopulmonary arrest and could not be resuscitated. A postmortem examination was performed to determine the cause of death. Postmortem computed tomography (CT) ruled out intracranial hemorrhage but revealed diffuse bilateral pulmonary consolidations and signs of bronchial obstruction. The autopsy revealed severe pulmonary edema and marked right ventricular hypertrophy. Microscopic examination of the lungs demonstrated characteristic features of pulmonary veno-occlusive disease (PVOD), including widespread fibrous intimal thickening and occlusion of small pulmonary veins and venules. Notably, there was no evidence of RA-associated interstitial lung disease (ILD). The direct cause of death was identified as pulmonary edema secondary to PVOD. This case highlights that PVOD can occur in patients with RA as a distinct pathological entity, independent of ILD. This finding is significant as it contrasts with previous reports where PVOD was associated with ILD. Therefore, clinicians should consider PVOD in the differential diagnosis of RA patients who present with unexplained pulmonary hypertension or progressive dyspnea, even in the absence of interstitial lung disease. Full article

Background and Objectives: Hand joint deformity remains a main cause impairing quality of life in rheumatoid arthritis (RA). This study aimed to investigate the association between biologic and targeted-synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) treatment and the prevalence of hand joint deformity in RA patients. Materials and Methods: This cross-sectional analysis included RA patients recruited between 2019 and 2024. Hand joint deformity was defined as the presence of specific deformity in any of 28 hand joints, including the metacarpophalangeal (MCP) joints I-V, proximal interphalangeal (PIP) joints I-V, and distal interphalangeal (DIP) joints II-V. The key exposure was the use of b/tsDMARDs. Multivariable logistic regression was used to assess the association between b/tsDMARDs treatment and hand joint deformity. Results: A total of 1083 RA patients with a mean age of 52.6 ± 12.4 years and a median disease duration of 5 (2,11) years were included. Hand joint deformity was present in 25.4% (275/1083) of patients. The top three deformed joint locations were PIP V (12.9%, 140/1083), PIP III (11.6%, 126/1083), and PIP IV (10.9%, 118/1083). The top three deformity types were ulnar deviation of MCP II-V (8.0%, 87/1083), boutonniere deformity of II-V fingers (6.8%, 74/1083), and swan neck deformity of II-V fingers (6.7%, 73/1083). In total, 17.4% (188/1083) of patients had received b/tsDMARDs. After 1:1 propensity score matching for age, sex, and disease duration, the prevalence of deformity was significantly lower in patients treated with conventional medicine (csDMARDs and/or GCs) add-on b/tsDMARDs compared to those treated with conventional medicine (27.1% vs. 61.7%, p < 0.001). Multivariable logistic regression analysis showed that b/tsDMARDs use was independently associated with a lower prevalence of hand joint deformity after adjusting for confounding factors (OR = 0.211, 95%CI: 0.129–0.345, p < 0.001). Conclusions: The use of b/tsDMARDs was independently associated with a lower prevalence of hand joint deformity in RA. Full article

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis. Full article