Search Results (225)

Chronic obstructive and inflammatory lung diseases share overlapping clinical manifestations and spirometric features, complicating differential diagnosis and monitoring. In this study, we performed an integrative real-time proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) breathomics analysis to assess whether exhaled volatile organic compound (VOC) profiles enable multiclass discrimination among bronchial asthma (BA), chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and lymphangioleiomyomatosis (LAM), with healthy individuals as controls. Breath VOC data from 843 subjects were analyzed using a stratified 70/30 train/test split. An ensemble feature selection strategy based on gradient boosting (XGBoost with SMOTE within cross-validation) identified stable VOC panels (top 25% selection probability), yielding 29 VOCs and 31 features including clinical covariates. On the independent test set, the VOC-only model achieved a macro-averaged one-vs-one (OvO) AUC of 0.866 (95% CI 0.829–0.903), while the combined model improved to 0.888 (95% CI 0.853–0.919), indicating modest value of clinical variables. Pairwise analysis demonstrated highest discrimination for CF (AUC up to 0.988), whereas BA and LAM showed lower sensitivity (<0.60), likely reflecting heterogeneity and limited sample size. Given differences in age, sex, BMI, and smoking status across cohorts, confounding effects were assessed, confirming that VOC signatures retain independent diagnostic information. Disease-specific VOC interaction networks revealed distinct remodeling patterns, with central metabolites not captured by univariate analysis. Overall, PTR-TOF-MS breathomics demonstrates proof-of-concept multiclass discrimination across chronic lung diseases. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Long non-coding RNAs (lncRNAs) are key regulators of gene expression and play critical roles in cancer-related signaling networks. Dysregulation of antagonistic lncRNAs may contribute to hepatocarcinogenesis and disease progression. This study investigated the clinical significance and predictive value of two biologically antagonistic lncRNAs, UFC1 and PTENP1, as circulating biomarkers for hepatocellular carcinoma (HCC) in an Egyptian cohort. Expression levels of these lncRNAs were quantified in 100 HCC patients and 100 age- and sex-matched healthy controls. UFC1 was significantly upregulated (~2.9-fold), while PTENP1 was markedly downregulated (~4-fold) in HCC patients, with a strong inverse correlation (r = −0.609, p < 0.001). Both lncRNAs demonstrated higher diagnostic accuracy compared to alpha-fetoprotein (AFP); combining them with AFP further enhanced overall performance. UFC1 expression was increased progressively with advancing fibrosis grade and Barcelona Clinic Liver Cancer (BCLC) stage, while PTENP1 levels diminished with BCLC stage. Logistic regression confirmed UFC1 as an independent risk factor and PTENP1 as a protective factor for HCC. In conclusion, the blood-based UFC1/PTENP1 panel exhibits promising diagnostic accuracy and is associated with disease severity, surpassing AFP. Their fibrosis-associated dysregulation suggests a role in early hepatocarcinogenesis. This antagonistic lncRNA signature represents a potential, non-invasive tool for HCC detection and risk stratification, meriting further clinical validation. Full article

Type 1 diabetes (T1D) is an autoimmune disease with a strong genetic component (~70% heritability). Early identification of individuals at risk is crucial for early intervention or risk assessment. Although polygenic risk scores (PRS) have shown promise in risk assessment, most current approaches remain constrained by linear assumptions and limited generalizability. We aimed to develop a neural network-driven classifier using T1D-associated single nucleotide polymorphisms (SNPs). In addition, we explored the inclusion of an entropy-derived feature as a complementary variable, representing the degree of genetic variability within an individual’s genotype profile across the 67 T1D-associated SNPs, to evaluate its potential additive contribution to the model performance. We analyzed genotype data from 11,909 individuals in the UK BioBank (546 T1D cases and 11,363 controls). Sixty-seven well-known SNPs associated with T1D were utilized as inputs to the model, using two distinct allele-encoding strategies. A feed-forward neural network was evaluated under varying case–control ratios through five-fold cross-validation. Performance was assessed using the area under the receiver operating characteristic curve (AUC) on a held-out test set and on an external European cohort as a validation cohort. Across five-fold cross-validation, the best configuration achieved a median AUC of 0.903. On the held-out UK Biobank test set, the model generalized well, with an AUC of 0.8889 (95% CI: 0.8516–0.9262). A probability-based risk framework, constructed using five risk groups (“very low”, “low”, “intermediate”, “high”, and “very high” risk), yielded a negative predictive value (NPV) of 98.9% for the “very low” risk group and a Positive Predicted Value (PPV) of 61.9% with a specificity of 97.3% for the “very high” risk group, assuming a 10% T1D prevalence. External validation in the German Diabetes Study reproduced clear case–control separation; for individuals with recent onset diabetes and glutamic acid decarboxylase antibodies (GADA+) vs. controls, specificity reached 91.9% in the “high” risk group (PPV of 94.3%) and 97.6% in the “very high” risk group (PPV of 95.7%). The proposed neural network reliably predicts T1D genetic risk using a compact SNP panel of 67 SNPs and maintains accuracy in both internal and external European cohorts. Its probabilistic output enables clinically interpretable risk thresholds, while entropy features contributed modestly to performance. These results demonstrate that a neural network-based approach achieves discriminative performance that is comparable to established T1D genetic risk models, while offering flexible probability-based risk stratification and architectural extensibility for future integration of additional features. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking targeted therapies and characterized by pronounced heterogeneity and widespread dysregulation of microRNAs (miRNAs) that influence epithelial-to-mesenchymal transition (EMT) and metastasis. Tumor-derived extracellular vesicles (tEVs) further contribute to TNBC progression by transporting oncogenic cargo that can enhance pro-inflammatory signaling. The synthetic SMRwt peptide has been suggested to modulate oncogenic pathways; however, its effects on EV miRNA composition and inflammatory transcript profiles in TNBC remain unclear. Here, we investigated whether SMRwt alters tEV-associated miRNAs and cytokine transcript signatures relevant to EMT and inflammasome-linked pathways. Extracellular vesicles were isolated from SMR-treated and untreated MDA-MB-231 cells, followed by nanoparticle tracking analysis and small RNA sequencing. SMRwt treatment enriched 11 tumor-suppressive miRNAs (including Let-7a-5p, Let-7b-5p, miR-24-3p, miR-26b-5p, miR-92a-3p, miR-93-5p, and miR-496) previously associated with the regulation of proliferation, EMT, migration, and metastasis. We also observed modest, non-significant decreases (1.01–1.27-fold) in oncogenic miR-1200, miR-374a-5p, and miR-937-3p, which have been implicated in the progression of breast, lung, and bone malignancies. Complementary transcriptomic profiling using the NanoString nCounter Breast Cancer 360 Gene Expression Panel (NanoString Technologies, Inc., Seattle, CA, USA) demonstrated reduced expression of inflammasome-associated cytokines in TNBC cells relative to non-tumorigenic controls, including a log₂ fold change of −1.15 for IL 1β (MDA-MB-231 vs. MCF10A). These transcript-level changes suggest potential modulation. Additionally, SMRwt suppresses ASC-mediated caspase-1 activation and reduces IL-1β secretion, thereby inhibiting NLRP3 inflammasome signaling. Therefore, we infer that SMRwt simultaneously restores tumor-suppressive miRNA networks and suppresses inflammasome-driven inflammation, supporting its potential as a dual-target therapeutic strategy for TNBC. Full article

Transfer pricing has become a major channel through which multinational enterprises shift profits across countries. This study examines the macroeconomic and institutional determinants of transfer pricing in seven European economies (France, Spain, Germany, the United Kingdom, Italy, the Netherlands, and Portugal) over the period 1985–2025. The main objective is to identify the key factors influencing profit shifting and to analyze the mechanisms through which multinational firms allocate profits across jurisdictions. The study employs panel data techniques and uses two different proxies to capture transfer pricing practices (trade-based and intangible-based channels). To analyze both long-run and short-run relationships between transfer pricing, exchange rate dynamics, foreign direct investment, inflation and institutional quality, the analysis relies on heterogeneous panel estimators and cointegration tests, supported by several robustness checks. The empirical results reveal the existence of a long-run relationship between transfer pricing and its macroeconomic and institutional determinants. Exchange rate fluctuations and inflation exert a negative effect on transfer pricing, whereas Foreign Direct Investment has a positive impact by expanding multinational investment networks and intra-group transactions. The effect of institutional quality, proxied by control of corruption, appears more heterogeneous and may vary across jurisdictions as well as across the type of transfer pricing channel, whether related to tangible trade or intangible assets. These results emphasize the importance of institutional quality and international tax coordination in limiting aggressive profit-shifting practices. Full article

Epileptogenesis is commonly defined by the emergence of spontaneous seizures after an initial insult; however, convergent experimental and clinical evidence indicates that the underlying disease process begins well before seizures become clinically detectable. During this pre-seizure phase, persistent molecular cascades remodel synaptic plasticity, circuit architecture, and glial–immune signaling. These processes are associated with trait-like alterations in cognition, affect, and behavior. Despite their clinical relevance, these neurobehavioral signatures remain poorly integrated into molecular models of epileptogenesis and are rarely considered as translational biomarkers of disease progression. This review synthesizes evidence linking core epileptogenic molecular cascades—maladaptive synaptic plasticity, glial–immune signaling, oxidative–metabolic stress, and activity-dependent gene regulation—to reproducible alterations in executive control, cognitive flexibility, emotional regulation, and motivational–social behavior. We outline an integrative framework in which these phenotypes are conceptualized as system-level readouts of progressive network reconfiguration rather than nonspecific “comorbidities” or mere consequences of recurrent seizures. Within this perspective, neurobehavioral markers can complement electrophysiological and molecular measures by capturing disease-relevant changes during windows when anti-epileptogenic interventions would be most effective. To increase mechanistic specificity, we provide representative pathway and gene-level anchors across epileptogenesis stages, a structured molecular-to-neurobehavioral mapping, and an operational biomarker panel specifying confounders and minimal controls. These anchors are included to ground the framework in experimentally documented molecular nodes with stage-dependent relevance; examples are representative rather than exhaustive, and evidence strength is indicated as preclinical mechanistic versus associative human observations. Finally, we discuss methodological requirements for biomarker validity (specificity, temporal anchoring, and cross-model consistency) and outline how integrating molecular and neurobehavioral trajectories may refine target discovery and improve the translation of anti-epileptogenic strategies. Conceptualizing epileptogenesis as a progressive disease process with measurable pre-seizure neurobehavioral signatures may broaden biomarker strategies beyond seizure occurrence and support the development of disease-modifying interventions. Full article

Older adults with disabilities face compounded vulnerabilities due to both functional limitations and socioeconomic disadvantage. In South Korea, where public welfare systems remain fragmented and cultural values emphasize independence and productivity, understanding the mechanisms linking socioeconomic status (SES) to health outcomes is critical. This study investigates whether reserve capacity mediates the relationship between SES and self-rated health (SRH) in older adults with disabilities. Data were drawn from the supplementary survey on people with disabilities in the 18th wave (2023) of the Korea Welfare Panel Study (KWePS). The analytic sample included older adults aged 65 and above with registered disabilities. A multiple mediation analysis was conducted using Model 4 of the PROCESS macro in SPSS to examine whether three dimensions of reserve capacity—intrapsychic resources (self-esteem), interpersonal resources (social support satisfaction), and tangible resources (use of public disability services)—mediated the relationship between SES and SRH. Demographic and health-related covariates were statistically controlled. The results are as follows: The direct effect of SES on SRH was not significant; however, significant indirect effects were found through all three mediators. Higher SES was positively associated with intrapsychic and interpersonal resources and negatively associated with tangible resource use. Among the mediators, interpersonal resources had the strongest positive effect on SRH, while tangible resources showed a negative association—possibly due to compensatory activation or increased disease awareness among service users. The findings highlight the importance of psychosocial and relational resources in shaping perceived health among disabled older adults in Korea. Policy interventions should move beyond material assistance and focus on strengthening social networks and psychological resilience to reduce health disparities in this population. Full article

Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization (AMSHL) algorithm, a novel approach that strategically combines fingerprinting-based and geometric time-difference-of-arrival (TDoA) methods through condition-aware adaptive fusion. The proposed framework employs a 4-RIS cooperative architecture with strategically positioned panels on room walls, enabling comprehensive spatial coverage and favorable geometric diversity. AMSHL incorporates five key innovations: (1) a hybrid fingerprint database combining received signal strength indicator (RSSI) and TDoA features for enhanced location distinctiveness; (2) a multi-stage cascaded refinement process progressing from coarse fingerprinting initialization through to iterative geometric optimization; (3) an adaptive fusion mechanism that dynamically adjusts algorithm weights based on real-time channel quality assessment including signal-to-noise ratio (SNR) and geometric dilution of precision (GDOP); (4) a robust iteratively reweighted least squares (IRLS) solver with Huber M-estimation for outlier mitigation; and (5) Bayesian regularization incorporating fingerprinting estimates as informative priors. Comprehensive Monte Carlo simulations at 3.5 GHz carrier frequency with 400 MHz bandwidth demonstrate that AMSHL achieves a median localization error of 0.661 m, root-mean-squared error (RMSE) of 1.54 m, and mean-squared error (MSE) of 2.38 m², with 87.5% probability of sub-2m accuracy, representing a 4.9× improvement over conventional hybrid fingerprinting in median error and a 7.1× reduction in MSE (from 16.83 m² to 2.38 m²). An optional sigmoid-based fusion variant (AMSHL-S) further improves sub-2m accuracy to 89.4% by eliminating discrete switching artifacts. Furthermore, we provide theoretical analysis including Cramér–Rao lower bound (CRLB) derivation with an empirical MSE comparison to quantify the gap between practical algorithm performance and theoretical bounds (MSE-to-CRLB ratio of approximately $4.0\times {10}^4$), as well as a computational complexity assessment. All reported metrics have been cross-validated for internal consistency across formulas, tables, and textual descriptions; improvement factors and error statistics are verified against primary simulation outputs to ensure reproducibility. The complete simulation framework is made publicly available to facilitate reproducible research in RIS-aided positioning systems. Full article

Background/Objectives: Circular RNAs (circRNAs) are emerging players in human diseases, with functions as part of competing endogenous networks. Given the importance of messenger RNA (mRNA) regulation in human diseases and the potential of circRNAs in this regulation, we studied the circRNA–mRNA couple in blood within a cohort of 712 patients suspected of having hereditary colorectal cancer (CRC) and 249 matched controls. Methods: The circRNA–mRNA couple was studied by SEALigHTS (Splice and Expression Analyses by exon Ligation and High-Throughput Sequencing) with a panel of 23 genes involved in CRC predisposition, comprising 788 probes designed at exon ends, enabling the exploration of all exon–exon junctions. Following reverse transcription and probe hybridization on cDNA, nearby probes were ligated, and the number of ligations was quantified using unique molecular identifiers and sequencing. Results: We identified 220 circular junctions, including 47 novel ones. The circRNA/mRNA ratio was 2.42-fold higher in patients compared to controls (p < 10⁻¹⁶), irrespective of age at cancer onset. This increase was mainly driven by POLD1 (fold change 3.84) and a single circPOLD1(3,2) with oncogenic potential Conclusions: This study supports the existence of a physiological balance between circRNA and mRNA that can be disrupted under pathological conditions. It rules out a competitive mechanism between circular and linear transcripts in CRC predisposition and raises questions about the role of specific circRNAs in the development of CRC, either as a cause or a consequence. Full article

Under the dual context of economic transformation and carbon peak and neutrality goals in Northeast China’s three provinces, old industrial bases in these regions are facing challenges such as fragmented green innovation resources and imbalanced cooperation, which constrain coordinated pollution and carbon reduction. This paper examines the mechanism between the urban green innovation network structure and synergistic pollution–carbon reduction efficiency in the region. Based on panel data from 34 prefecture-level cities (2013–2022), this paper employs social network analysis to characterize the green innovation network, a super-efficient SBM model to evaluate synergistic pollution–carbon reduction efficiency, and the Haken model to reveal the dynamic evolution mechanism. Results show that the green innovation network is fragmented and uneven, with significant efficiency disparities between the Central–Southern Liaoning and Harbin–Changchun urban agglomerations. A multi-core radiating network centered on Shenyang, Dalian, and Changchun has begun to form, alongside a rise in synergistic efficiency from 0.56 to 0.82. Further analysis identifies a mutually reinforcing mechanism: the green innovation network enhances synergistic efficiency mainly by increasing network density, while synergistic efficiency promotes the network by strengthening centrality. The findings provide pathways for Northeast China to achieve coordinated pollution control and carbon reduction through optimizing innovation networks. Full article

We present FingerType, a one-handed text input method based on thumb-to-finger gestures. FingerType detects tap events from 3D hand data using a Temporal Convolutional Network (TCN) and decodes the tap sequence into words with an n-gram language model. To inform the design, we examined thumb-to-finger interactions and collected comfort ratings of finger regions. We used these results to design an improved T9-style key layout. Our system runs at 72 frames per second and reaches 94.97% accuracy for tap detection. We conducted a six-block user study with 24 participants and compared FingerType with controller input and touch input. Entry speed increased from 5.88 WPM in the first practice block to 10.63 WPM in the final block. FingerType also supported more eyes-free typing: attention on the display panel within ±15° of head-gaze was 84.41%, higher than touch input (69.47%). Finally, we report error patterns and WPM learning curves, and a model-based analysis suggests improving gesture recognition accuracy could further increase speed and narrow the gap to traditional VR input methods. Full article

The total cavopulmonary anastomosis (Fontan procedure), a palliative procedure for single-ventricle congenital heart disease, improves survival but is associated with progressive multiorgan complications and high long-term morbidity. Prior blood-based proteomic studies in adults have been limited to targeted antibody-based panels or focused on methodological comparisons. Systemic molecular alterations in younger, clinically heterogeneous patients, particularly in untargeted pathways, remain incompletely characterized. Serum samples from 48 Fontan patients and 48 age- and sex-matched healthy controls were analyzed using mass spectrometry with TMT labeling. 2228 proteins were quantified, of which 124 were significantly differentially abundant (fold change > 1.5 or <0.67, FDR-adjusted p < 0.05). Network analysis identified three major functional clusters: extracellular matrix (ECM) organization (predominantly increased), actin cytoskeleton organization, and platelet-related pathways (both predominantly decreased). Stratified analyses showed reduced ECM protein abundance in high-risk patients, suggesting a shift from active remodeling toward a more established fibrotic state, and uniquely elevated cytochrome b5 reductase 3 (CYB5R3), implicating altered redox homeostasis, nitric oxide metabolism, and cellular aging. Overall, our findings extend prior targeted analyses, reveal potential biomarkers such as CYB5R3 and underscore the complexity of the Fontan circulation, with implications for risk stratification and therapeutic targeting. Full article

Background: Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental condition influenced by both genetic and non-genetic factors, although the underlying pathomechanisms remain unclear. We systematically analyzed microRNA (miRNA) expression and associated functional pathways in ASD to evaluate their potential as prenatal/postnatal, diagnostic, and prognostic biomarkers. Methods: Peripheral blood mononuclear cells from 12 Sicilian patients with ASD (eight with normal cognitive function) and 15 healthy controls were analyzed using small RNA sequencing. Differential expression analysis was performed with DESeq2 (|fold change| ≥ 1.5; adjusted p ≤ 0.05). Functional enrichment and network analyses were conducted using Ingenuity Pathway Analysis, focusing on Diseases and Biofunctions. Results: 998 miRNAs were differentially expressed in ASD, 424 upregulated and 553 downregulated. Enriched pathways were primarily associated with psychological and neurological disorders. Network analysis highlighted three principal interaction clusters related to inflammation, cell survival and mechanotransduction, synaptic plasticity, and neuronal excitability. Four miRNAs (miR-296-3p, miR-27a, miR-146a-5p, and miR-29b-3p) emerged as key regulatory candidates. Conclusions: The marked divergence in miRNA expression between ASD and controls suggests distinct regulatory patterns, thus reinforcing the central involvement of inflammatory, autoimmune, and infectious mechanisms in ASD, mediated by miRNAs regulating S100 family genes, neuronal migration, and synaptic communication. However, rather than defining a predictive biomarker panel, this study identified candidate miRNAs and regulatory networks that may be relevant to ASD pathophysiology. As such, further validation in appropriately powered cohorts with predictive modeling frameworks are warranted before any biomarker or diagnostic implications can be inferred. Full article