Abstract: The aim of this study was to synthesise qualitative evidence from family members’ experiences of long-term home care for older adults provided by live-in migrant caregivers. We conducted a systematic literature review with meta-synthesis using four online databases. The search included articles published between January 2015 and November 2025 on the CINAHL, PubMed, SCOPUS and WOS databases. Thematic synthesis of qualitative data was conducted. Results: eleven papers from six different countries fulfilled the criteria and were included in the thematic synthesis. Four main themes were identified: 1. Not an easy decision. 2. A stranger at the heart of family life. 3. Two worlds that meet and need each other. 4. Improving the integration of migrant caregivers into family life. Hiring migrant caregivers to provide long-term home care to older adults can ease the burden on family caregivers, but it is an additional source of stress and worry. The family members of older adults call for greater financial and institutional support, as well as the involvement of social and health services in the training and education of families and migrant caregivers. La capacidad de negociación y llegar a consensos entre OAs, familiares y cuidadores migrantes residents, son claves para mejorar la convivencia y cuidados a OAs. Negotiation skills and the ability to reach consensus between older adults (OAs), family members and resident migrant caregivers are key to improving cohabitation and care for OAs. The primary goal is the well-being of the OAs, which involves overcoming cultural prejudices, learning together in response to the new situation, improving caregivers’ training, and ensuring continuity of care.

Abstract: Introduction: Post-Orgasmic Illness Syndrome (POIS) is a rare, debilitating condition characterized by a constellation of systemic, allergic, cognitive, and emotional symptoms occurring shortly after orgasm. These symptoms severely impact sexual functioning and quality of life. Despite its profound effects, POIS remains poorly understood, underrecognized, and without a definitive treatment.Case Presentation: We report two cases observed in a specialized sexology consultation. The first case concerns a 49-year-old married man presenting with secondary-onset POIS. His symptoms, including muscle rigidity, genital burning, and profound fatigue, consistently emerged after ejaculation, whether during intercourse or masturbation. Extensive investigations revealed no abnormalities. Management included pharmacotherapy (fluvoxamine 50 mg daily and cyclobenzaprine 10 mg daily), psychological support, and sexual counseling. The patient adapted sexual practices, including adopting a lateral coital position to minimize exposure to seminal fluid. Although symptoms persisted after each orgasm, these interventions led to an overall improvement in quality of life.The second case involves an 18-year-old male experiencing primary-onset POIS since puberty. He developed severe fatigue, cognitive dysfunction, low-grade fever sensations, irritability, and abdominal discomfort following ejaculation. Comprehensive evaluations excluded infectious and allergic causes. Notably, the patient required psychiatric hospitalization after a severe behavioral disturbance characterized by agitation and hetero-aggressiveness post-orgasm. Treatment involved antipsychotic and antidepressant therapy, psychological support and psychoeducation.Conclusion: These cases highlight the heterogeneous and distressing nature of POIS and its profound impact on sexual health and psychosocial functioning. Multidisciplinary management combining pharmacological, psychological, and sexual interventions may yield partial symptom control and improve patients' quality of life. Increased clinician awareness and further research are urgently needed to advance understanding, diagnosis, and therapeutic strategies for POIS.

Abstract: In Candida species, including Candidozyma auris (formerly Candida auris), overexpression of efflux pumps is a well-established mechanism of antifungal resistance. However, accumulating evidence indicates that impaired drug import may also significantly contribute to reduced antifungal susceptibility. Sugar importers, historically viewed solely as hexose transporters (HGTs), are now emerging as potential indirect modulators of antifungal uptake. Here, we performed a comprehensive inventory and functional analysis of the HGT family in C. auris to assess its contribution to antifungal import. Phylogenetic analyses revealed that C. auris HGTs are more closely related to those of Candida albicans (C. albicans) than Saccharomyces cerevisiae (S. cerevisiae). All HGT genes showed basal expression, with several significantly downregulated upon fluconazole (FLC) exposure. To establish functional relevance, we generated a mini-library of HGT deletion mutants. Notably, the Δhgt13 strain exhibited markedly increased FLC resistance, concomitant with reduced intracellular FLC accumulation and decreased membrane permeability. Consistently, molecular docking and molecular dynamics simulations demonstrated strong and stable interactions between FLC and Hgt13p. Together, these findings implicate Hgt13p as a key determinant of FLC import and membrane permeability, revealing reduced FLC import could also contribute to antifungal resistance in C. auris.

Abstract: Individual phishing URLs are often short-lived, but underlying infrastructure such as domains, IP addresses, and certificates exhibits recurring patterns. We propose a graph-based detection framework that models a heterogeneous network comprising domains, IP addresses, TLS certificates, and registrars. Node embeddings are learned using a relational graph convolutional network (R-GCN) trained on 3.1 million domains, of which 210,000 are labeled as phishing-related. Structural features such as shared-IP communities, certificate reuse, and registrar clusters are incorporated into the model. The graph-based detector is capable of flagging suspicious domains before they are widely used in attacks; in a retrospective study, it identifies 73% of phishing domains at least 24 hours prior to first appearance in blacklists. Compared with domain-lexical baselines, the method improves precision at 90% recall by 15.6 percentage points. These findings demonstrate that infrastructure-level graph modeling provides complementary signals to content-based phishing detection and can enhance proactive defense.

Abstract: The growing emphasis on environmental sustainability and the need for advanced manufacturing methods have accelerated progress in materials processing. Aluminum powder metallurgy (APM) is particularly promising due to aluminum’s low density, high strength-to-weight ratio, and the inherent benefits of the powder metallurgy (PM) process. However, the corrosion resistance of sintered aluminum components remains a significant concern. In this study, shot peening (SP) was employed as a surface modification technique to improve the corrosion behavior of Alumix 321 PM alloy. Sampleas of the as-sintered and shot peened Alumix 321 PM alloy, together with the wrought alloy counterpart AA6061, were characterized using non-contact optical profilometry, optical microscopy (OM), and scanning electron microscopy (SEM). Corrosion performance was evaluated in 3.5 wt.% NaCl solution using Tafel extrapolation (TE), cyclic polarization (CP), stair-step polarization (SSP), and electrochemical impedance spectroscopy (EIS). The results revealed that shot peening increased surface roughness and significantly reduced the corrosion rate from 0.079 mmpy to 0.004 mmpy for the unpeened and peened samples, respectively. While pitting was the dominant corrosion mechanism in the wrought alloy, the PM alloy exhibited a combination of pitting, crevice, and intergranular corrosion. These findings highlight the potential of SP in enhancing the durability of aluminum-based PM components, offering valuable insights for industrial applications.

Abstract: Background: Achilles tendinopathy (AT) is a debilitating condition with limited therapeutic options in patients contraindicated for corticosteroids. Injective collagen has emerged as a promising alternative, yet evidence in fragile populations such as diabetics remains scarce. Objective: Aim of present work was to evaluate the clinical effectiveness of Porcine collagen injections in diabetic patients with chronic AT, looking for the pain reduction and increase of functionality. Methods: Twenty-two diabetic patients with degenerative Achilles tendinopathy unresponsive to treatment were included were splited in two groups, according the type of AT pathology: Insertional (I) and non-insertional (NI). Patients received five weekly peritendinous injections of porcine collagen. Outcome measures included VAS (baseline, post-2nd injection, 1 month, 6 months), VISA-A at 6 months, return-to-activity time and adverse events monitoring. Additional variables included BMI, HbA1c, symptom duration, and previous treatments. Analyses included descriptive statistics, paired t-tests, regression models, and ANOVA tests. Results: All patients completed the protocol with no adverse events. Mean VAS decreased significantly from baseline to 6 months in both AT-I and AT-NI patients (mean delta VAS: 5.1 and 4.4, respectively; p=0.001). Mean delta VISA-A scores were 32.78 and 38.97 in AT-I and AT-NI (p<0.0001) and median return to work (RTW) were 37 and 35 days in AT-I and AT-NI, respectively (p=ns). No significant differences were discovered comparing AT-I vs AT-NI, in terms both VAS and VISA-A variation per ml of injected product (p=ns). Conclusions: Porcine collagen is a safe, effective, and sustainable treatment for Achilles tendinopathy in diabetic patients with both AT-I and AT-NI conditions. This study supports its adoption as a first-line of conservative approach for pain reduction and functionality improvement.

Abstract: Autoimmune rheumatic diseases arise when the immune system transitions from a flexible, self-regulating network into a metabolically and epigenetically fixed inflammatory attractor state. This review synthesizes emerging evidence that immune tolerance is governed by a coupled epigenetic–metabolic axis integrating mitochondrial fitness, chromatin accessibility, redox balance, and nutrient flux across lymphoid, myeloid, and stromal compartments. We examine how chronic cytokine signaling, hypoxia, and oxidative stress destabilize regulatory programs, imprint glycolytic effector states, and remodel enhancer landscapes, thereby sustaining autoreactive circuits even after inflammatory pathways are pharmacologically suppressed. Multi-omic and spatial analyses reveal that pathogenic chromatin architectures, persistent mitochondrial dysfunction, and intercellular metabolite exchange cooperate to establish self-sustaining inflammatory ecosystems in rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, and Sjögren’s syndrome. We further highlight therapeutic strategies aimed at tolerance reprogramming, including metabolic correction, chromatin-targeted agents, CAR-Tregs, tolerogenic dendritic cells, and integrative biomarkers that quantify metabolic–epigenetic coherence. By reframing autoimmunity as a disorder of energetic and chromatin desynchronization rather than isolated immune activation, this review outlines a mechanistic path toward durable, drug-free remission through deliberate restoration of the molecular architecture that maintains immune self-recognition.

Abstract: Human action recognition (HAR) remains challenging, particularly for skeleton-based methods due to issues like domain shift and limited deep semantic understanding. Traditional Graph Convolutional Networks often struggle with effective cross-domain adaptation and inferring complex semantic relationships. To address these limitations, we propose CD-SEAFNet, a novel framework meticulously designed to significantly enhance robustness and cross-domain generalization for skeleton-based action recognition. CD-SEAFNet integrates three core modules: an Adaptive Spatio-Temporal Graph Feature Extractor that dynamically learns and adjusts graph structures to capture nuanced spatio-temporal dynamics; a Semantic Context Encoder and Fusion Module which leverages natural language descriptions to inject high-level semantic understanding via a cross-modal adaptive fusion mechanism; and a Domain Alignment and Classification Module that employs adversarial training and contrastive learning to generate domain-invariant, yet discriminative, features. Extensive experiments on the challenging NTU RGB+D datasets demonstrate that CD-SEAFNet consistently outperforms state-of-the-art methods across various evaluation protocols, unequivocally validating the synergistic effectiveness of our adaptive graph structure, semantic enhancement, and robust domain alignment strategies.

Abstract: This paper introduces an enhanced version of Dr.Scratch, a web-based tool for automatically assessing Compu-tational Thinking (CT) skills evident in visual programmingprojects. We propose a fuzzy logic-based scoring framework toaddress limitations in existing rule-based assessment systems.The paper reviews relevant prior initiatives, details the analyticalframework employed to interpret Scratch code, and elucidatesthe computational aspects considered when deriving a CT scorefrom user-created artifacts. Our methodology integrates fuzzyinference to generate continuous, explainable scores across coreCT dimensions. Experimental analysis of over 250 Scratchprojects demonstrates that the fuzzy scoring model providesfiner granularity, better alignment with educator evaluation, andimproved interpretability compared to deterministic approaches.We present preliminary findings from our investigation, discussfuture directions, and address current limitations in automatededucational assessment. The contributions of this work advancethe field of Educational AI toward more nuanced, pedagogicallygrounded computational thinking assessment.

Abstract: This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by an unmanned aerial vehicle (UAV). A DJI Matrice 300 RTK equipped with a Zenmuse P1 camera is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment.From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning.

Abstract: Background: Arterial hypertension and increased blood pressure variability (BPV) are major prognostic determinants in patients with ischemic heart disease (IHD). While exercise training is known to improve blood pressure (BP) control, the effects of different combined exercise modalities on BPV in IHD remain poorly defined. This randomized pilot study compared the effects of continuous combined training (CCT; moderate-intensity continuous aerobic exercise plus resistance training) and interval combined training (ICT; high-intensity interval aerobic exercise plus resistance training) on BPV and BP parameters in hypertensive patients with IHD. Methods: Thirty-six clinically stable patients with IHD and hypertension were randomized to CCT or ICT for 12 weeks. Outcomes included short-term BPV assessed by 24-hour ambulatory BP monitoring, resting and 24-hour BP, and exercise capacity. Results: Short-term systolic BPV significantly decreased in the CCT group but remained unchanged in the ICT group: [adjusted between-group difference −2.1 mmHg (95% CI: −4.1 to −0.1; p 0.029]. Resting systolic BP decreased similarly in both groups, whereas no significant changes were observed in 24-hour BP values. Peak oxygen uptake improved in both groups with a greater increase in the ICT group [adjusted between-groups difference +1.7 mL·kg⁻¹·min⁻¹ (95% CI: 0.7 to 2.8); p = 0.032). Conclusion: These findings suggest that, in patients with IHD, continuous combined training may be more effective than interval combined training in reducing short-term BPV, whereas interval training may confer greater improvements in aerobic capacity. Further adequately powered studies are warranted to confirm these results.

Abstract: Growing global populations and the rapid increase in older adults are driving healthcare costs upward. In response, the healthcare system is shifting toward models that allow for continuous monitoring of individuals without requiring hospital ad-mission. Advances in sensing technologies, embedded systems, wireless communica-tion, nanotechnology, and device miniaturization have made it possible to develop smart systems that continuously track human activity. Wearable sensors can monitor physiological indicators and other symptoms, helping to detect unusual or unexpected events. This enables timely assistance when it is needed most. This paper outlines these challenges and reviews recent developments in wearable sensor–based human activity monitoring systems. The focus is on health monitoring applications, including relevant biomarkers, wearable and implantable sensors, estab-lished sensor technologies currently used in healthcare, and the future prospects and challenges involved in researching, developing, and applying these sensors to support widespread use in human health monitoring.

Abstract: Sulforaphane (SFN), an aliphatic isothiocyanate derived from cruciferous vegetables such as broccoli, has emerged as a chemopreventive dietary agent. SFN exerts multifaceted anticancer effects by the activation of the nuclear factor erythroid 2-related factor 2 (Nrf2)–antioxidant response element (ARE) pathways, inhibition of histone deacetylases (HDACs) and hypoxia-inducible factor-1α (HIF-1α), and regulation of apoptosis and autophagy. Epidemiological studies have consistently associated cruciferous vegetable intake with reduced cancer risk, while mechanistic research has elucidated the capacity of SFN to modulate redox balance, detoxification pathways, and epigenetic processes. Recent clinical trials have further demonstrated its potential to reduce carcinogenic biomarker levels and support metabolic detoxification. This review integrates evidence from epidemiological observations, molecular mechanisms, and clinical studies to provide a comprehensive understanding of the role of SFN in cancer prevention and therapy. Finally, translational challenges, including limited bioavailability, dose optimization, and standardization of broccoli-derived preparations, are discussed as critical factors for advancing SFN from bench to bedside.

Abstract: The institutional drive to deploy digital assistive technologies—from IoT monitoring to AI companions—as a solution to the ageing care crisis functions as an ethical double-edged sword. This article argues that beyond isolated risks, these technologies introduce a systemic tension where gains in safety and efficiency often come at the cost of autonomy, human connection, and equity. We propose a critical framework that diagnoses four interconnected dimensions of this tension: (1) the erosion of privacy and autonomy through pervasive surveillance; (2) the risk of dehumanization in high-tech, low-touch interactions; (3) the "digital grey divide" as a social determinant of health; and (4) the perpetuation of "coded ageism" through algorithmic bias. To bridge the gap between ethical principle and practice, the framework translates this diagnosis into a practical roadmap for "Dignity-by-Design." It operationalizes person-centred care through three actionable shifts: moving from compliance to commitment, replacing static consent with dynamic engagement, and establishing the lived experience of older adults and caregivers as a core design standard via participatory action research. Ultimately, this work provides a critical tool for researchers, developers, and policymakers to guide the ethically-aligned implementation of technologies that truly enhance autonomy, foster trust, and uphold dignity in geriatric care.

Abstract: This study investigates how a Long Short-Term Memory (LSTM) model inter-nally represents baseflow contributions in snowmelt-driven, semi-arid mountain basins with heterogeneous geologic characteristics. Five basins in the Sangre de Cristo Mountains of northern New Mexico, spanning fractured Precambrian bedrock and sedimen-tary-volcanic terrain, were used to evaluate both model performance and interpretability. Baseflow dynamics were inferred post hoc using the Baseflow Index (BFI) and a two-reservoir HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. Although baseflow was not explicitly included in model training, internal cell state activations showed strong correlations with both shallow and deep baseflow com-ponents derived from the HEC-HMS model. To better understand how these relationships may change under climatic stress, BFI-based baseflow patterns were further analyzed un-der pre-drought and drought conditions. Results indicate that the LSTM learned to inter-nally distinguish between short- and long-residence flowpaths, encoding physically meaningful hydrologic behavior. This work demonstrates the potential for LSTM models to offer valuable insights into baseflow generation and groundwater–surface water inter-actions, particularly critical in water-scarce regions facing increasing drought frequency.

Abstract: Speech Emotion Recognition (SER) faces significant generalization challenges, particularly in Cross-Linguistic SER (CLSER), due to linguistic and cultural variabilities. Existing approaches struggle with robustly fusing diverse features and adapting to cross-linguistic discrepancies. To address this, we propose the Adaptive Contextualized Multi-feature Fusion Network (ACMF-Net), a novel architecture built on a ``contextualize first, then adaptively fuse'' paradigm. ACMF-Net leverages HuBERT embeddings alongside contextualized Mel-frequency Cepstral Coefficients (MFCCs) and prosodic features, each processed by dedicated Transformer encoders to capture rich temporal dependencies. A core innovation is the Dynamic Gating mechanism, which intelligently learns to dynamically weight the contributions of these heterogeneous feature modalities based on the input speech characteristics, thereby enhancing robustness against cross-linguistic variations. Evaluated on the IEMOCAP dataset for source language performance, ACMF-Net achieved superior Unweighted Accuracy (UAR), outperforming strong baselines and existing multi-feature fusion models. Furthermore, through few-shot fine-tuning on diverse target languages, ACMF-Net consistently demonstrated superior cross-linguistic generalization. An ablation study confirmed the critical contribution of each proposed component, especially the Dynamic Gating mechanism. These results underscore ACMF-Net's potential to significantly advance robust and generalized emotion recognition across linguistic boundaries.

Abstract: We investigate temperature fluctuations in hot QCD matter using a 3-flavor Polyakov-loop extended Nambu--Jona-Lasinio (PNJL) model. The high-order cumulant ratios $R_{n2}$ ($n>2$) exhibit non-monotonic variations across the chiral phase transition, characterized by slight fluctuations in the chiral crossover region and significant oscillations around the critical point. In contrast, distinct peak and dip structures are observed in the cumulant ratios at low baryon chemical potential. These structures gradually weaken and eventually vanish at high chemical potential as they compete with the sharpening of the chiral phase transition, particularly near the critical point and the first-order phase transition. Our results indicate that these non-monotonic peak and dip structures in high-order cumulant ratios are associated with the deconfinement phase transition. This study quantitatively analyzes temperature fluctuation behavior across different phase transition regions, and the findings are expected to be observed and validated in heavy-ion collision experiments through measurements of event-by-event mean transverse momentum fluctuations.

Abstract:

Damage to the chorda tympani (CT) nerve through trauma or experimental nerve axotomy results in the degeneration of anterior taste buds and taste loss. Our previous work demonstrated that Il1r signaling is required for taste bud regeneration and the recovery of taste function. However, the effects of experimental axotomy on immune responses in the absence of Il1r signaling remain unclear. To test this, we performed unilateral CT sectioning in Il1r KO or wild-type mice as previously described. We found that CD45+ immune cells, CD68+ and CD206+ M2-like macrophages are significantly increased near anterior taste buds at day two post-injury in wild-type but not Il1r KO mice. By day 5, these macrophage responses were slightly elevated in wild-type mice but remained at baseline levels in KO mice, indicating that immune responses to injury were suppressed rather than delayed in the absence of Il1r signaling. However, taste buds degenerated at similar time points in both strains. These results suggest that delayed taste bud degeneration in Il1r KO mice is not the primary reason for later functional deficits, though suppressed immune response may have other consequences in the injured peripheral taste system.

Abstract: Background: West Nile Virus (WNV) is a mosquito-borne flavivirus responsible for seasonal outbreaks in temperate and tropical regions, including Europe, the Mediterranean, and the Middle East. Its transmission via mosquitoes, particularly Culex species, poses persistent challenges to public health. Despite ongoing efforts, comprehensive prevention and treatment strategies remain limited. Methods: A comprehensive search of peer-reviewed literature, clinical trials, and government surveillance data was conducted using PubMed, Scopus, Web of Science, and supplementary web-based resources. Inclusion criteria focused on molecular studies of WNV, vaccine and antiviral drug development, and regional outbreak reports. Results: WNV transmission is influenced by climatic conditions, vector distribution, and ecological patterns. While human vaccines are currently under development, only veterinary vaccines resulted promising but still partial insights. Remarkably, therapeutic measures are currently limited to supportive care, whereas investigational antiviral drugs are in early-stage trials. Interestingly, Italy demonstrates robust surveillance with regular reporting of outbreaks, whereas data from Iran indicate that despite a widespread serological footprint, especially in southern and southwestern provinces, but the reported clinical impact on humans and animals appears comparatively less severe. Conclusions: Bridging gaps in vaccine availability, therapeutic innovation, and disease monitoring is essential for effective WNV management to prepare for potential severe future outbreaks in Europe and the Middle East. Regional differences between Italy and Iran reveal the need for tailored public health interventions, enhanced surveillance, and sustained investment in research. In our view, collaborative frameworks across Mediterranean and Middle Eastern countries in a “One Health” approach may improve preparedness and response to future WNV outbreaks.

Abstract: This research uses the stakeholder theory and dynamic capabilities theory to evaluate the relationship between stakeholder pressure, digital innovation, motivation, and the adoption of low-carbon operation management in enhancing manufacturing firms’ carbon reduction performance. A web-based survey of 412 Chinese manufacturing firms was used to analyze our conceptual model. This investigation implemented a hybrid approach known as structural equation modelling-artificial neural network (SEM-ANN) that involved two phases. Our research indicates that stakeholder pressures significantly motivate the emergence of low-carbon operations management practices and digital innovation, which has an auspicious correlation with motivation and low-carbon operation management. The study found mixed results regarding the impact of motivations on adopting low-carbon operations. While motivations significantly affected the adoption of low-carbon logistics, they did not significantly impact the emergence of low-carbon processes. Furthermore, the emergence of low-carbon products negatively impacted the firm’s carbon reduction performance, while low-carbon processes and logistics positively impacted the firm’s carbon reduction performance. A significant relationship between the adoption of digital innovation and low-carbon operations management practices, as well as the carbon reduction performance of the firm, are significantly correlated. Stakeholder pressure significantly impacts a firm’s carbon reduction performance. A contribution of the study is to advance stakeholder theory and dynamic capabilities theory frameworks in the context of sustainability initiatives for industries.