Search Results (476)

Group 1 of PAH patients encompasses patients with a diverse underlying etiological condition, having histological modifications that can affect gas exchange across the alveolar-capillary membrane, as reflected by decreased DLCO. Values of DLCO did not identify the exact reason for their decrease, but they can provide insights into the underlying pathobiology and prognosis of PAH patients. Our aim was to explore whether PAH patients with low DLCO constitute a different subpopulation and describe their characteristics and response to treatment. A total of 69 PAH patients were studied retrospectively and divided into two groups: group 1: DLCO ≥ 45% and group 2: DLCO < 45%. IPAH and PAH-CTD mainly constituted our population. The proportion of IPAH to PAH-CTD was almost the same between the two groups. Patients in group 2 were older (66.83 ± 11.61 vs. 59.27 ± 111.90, p = 0.035), mostly male (47.5% vs. 11.5% p = 0.008), and ever smokers (59% vs. 22%, p = 0.049). They mainly had WHO-FC III (68% vs. 32%) and had received more advanced therapy (40% on triple combination therapy vs. 16%). The two groups had similar mean PAP (group 1 = 32 (22.00–38.00) vs. group 2 = 35 (28.50–48.50) mmHg), while PVR was higher in group 2 (6.49 (4.10–9.52) vs. 3.61 (2.95–5.22) WU). In group 2, neither IPAH nor PAH-CTD patients improved WHO-FC, 6MWD, or NT-proBNP after treatment. In our center, PAH patients with low DLCO had some distinct clinical characteristics, such as poor prognosis and poor treatment response to vasodilatory therapy. Understanding the role of DLCO in both phenotyping PAH patients and in treatment response would be useful in guiding therapeutic approaches, especially now that new therapeutic targets are involved in PAH treatment. Full article

Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics data have shown promise in predicting pharmacological response, selecting the features with the highest predictive power critically affects model performance and biological interpretability. This study aims to compare computational and biologically informed gene selection strategies for predicting drug response in cancer cell lines and to propose a feature selection strategy that optimizes performance. Methods: Using gene expression and drug response data, we trained models on both data-driven and biologically informed gene sets based on the drug target pathways to predict IC₅₀ values for seven anticancer drugs. Several feature selection methods were tested on gene expression profiles of cancer cell lines, including Recursive Feature Elimination (RFE) with Support Vector Regression (SVR) against gene sets derived from drug-specific pathways in KEGG and CTD databases. The predictability was comparatively analyzed using both AUC and IC₅₀ values and further assessed on proteomics data. Results: RFE with SVR outperformed other computational methods, while pathway-based gene sets showed lower performance compared to data-driven methods. The integration of computational and biologically informed gene sets consistently improved prediction accuracy across several anticancer drugs, while the predictive value of the corresponding proteomic features was significantly lower compared with the mRNA profiles. Conclusions: Integrating biological knowledge into feature selection enhances both the accuracy and interpretability of drug response prediction models. Integrative approaches offer a more robust and generalizable framework with potential applications in biomarker discovery, drug repurposing, and personalized treatment strategies. Full article

This study aims to elucidate the neurodevelopmental toxicity and molecular mechanisms of endocrine-disrupting chemicals (EDCs) in neurodevelopmental disorders (NDDs) through a network toxicology approach, using triclosan exposure as a case example. Potential targets of triclosan were identified via comparative analysis of toxicogenomics databases such as the Comparative Toxicogenomics Database (CTD), Similarity Ensemble Approach (SEA), SwissTargetPrediction, and TargetNet. NDD-related targets were retrieved from GeneCards, Disease Gene Network (DisGeNET), and Online Mendelian Inheritance in Man (OMIM), resulting in 633 overlapping genes associated with disease pathology and triclosan effectors. Protein–protein interaction networks were constructed using STRING and Cytoscape, applying median-based algorithms to identify six core genes: AKT1, TP53, EGFR, FN1, SRC, and ESR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses via Metascape revealed that triclosan-induced NDDs are primarily associated with endocrine signaling disruption and activation of the PI3K-Akt pathway. Molecular docking with CB-Dock2 demonstrated strong binding affinities between triclosan and the core targets, while YASARA molecular dynamics simulations confirmed stable interactions, notably with EGFR, exhibiting high binding stability. Collectively, these findings delineate the potential molecular mechanisms underlying triclosan-induced NDDs and underscore the utility of network toxicology, molecular docking, and molecular dynamics simulations in assessing neurotoxicity and related molecular pathways. This research provides novel insights for future investigations, enhances understanding of the potential impact of neurodevelopmental disorders on health, and lays a scientific foundation for the development of preventive and therapeutic strategies. Full article

Background/Objectives: Intrinsically disordered protein regions (IDRs) are difficult to study due to their flexible nature and transient interactions. Computational folding using AlphaFold may offer one way to explore potential folding of these regions under various conditions. Human DNA topoisomerase IIα (TOP2A) is an essential enzyme involved in regulating DNA topology during replication and cell division. TOP2A has an IDR at the C-terminal domain (CTD) that has been shown to be important for regulating TOP2A function, but little is known about potential conformations that it may undertake. Methods: Utilizing the AlphaFold 3 (AF3) model by way of AlphaFold Server, TOP2A was folded as a dimer first without and then with 29 literature-supported post-translational modifications (PTMs) and DNA to observe whether there is predicted folding. Results: TOP2A CTD does not fold in the absence of PTMs. With the addition of PTMs, however, the CTD is predicted to fold into a globular bundle of loops and α-helices. While DNA alone did not induce folding, in the presence of PTMs, DNA ligands increased helicity of the folded CTD and caused it to interact at different core domain interfaces. In addition, DNA is predicted to enable folding of the TOP2A CTD in the presence of fewer PTMs when compared to the absence of DNA. Conclusions: AF3 predicts the folding of TOP2A CTD in the presence of specific PTMs, and this folding appears to shift to allow binding to DNA in functionally relevant regions. These studies provide predicted folding patterns that can be tested by biochemical approaches. AF3 may support the development of testable hypotheses regarding IDRs and enables researchers to model protein-DNA interactions. Full article

Background/Objectives: Binding of bactericidal/permeability-increasing (BPI) protein to Gram-negative (GN) bacteria plays a major role in bacterial elimination. The relationship between BPI-antineutrophil cytoplasmic autoantibodies (ANCA), persistent infections and immunoinflammatory diseases has not been elucidated. Methods: In total, 193 ANCA-positive patients detected by IIF with ANCA-associated vasculitides (AAV, n-40), connective tissue diseases (CTD, n-28), drug-induced vasculitides (DIV, n-17), ulcerative colitis (UC, n-24), UC with primary sclerosing cholangitis (UC/PSC, n-14), Crohn’s disease (CD, n-10), autoimmune hepatitis (AIH, n-19) and chronic infections (n-41) were tested using the BPI-ANCA quantitative and semiquantitative ELISA (ANCA-profile: BPI, proteinase 3, myeloperoxidase, elastase, cathepsin G, lactoferrin). BPI-ANCA were analyzed in 52 healthy persons. Results: A total of 46/193 (23.8%) patients had BPI-ANCA positivity. BPI-ANCA were more frequently present in patients with prolonged GN bacterial infections and inflammatory bowel diseases than in AAV, DIV, AIH, CTD and healthy controls (p < 0.001). UC/PSC patients more frequently had BPI-ANCA than UC and CD patients (p < 0.001). GN bacterial infections more frequently had BPI-ANCA than Gram-positive bacterial infections (p < 0.001). Infections caused by Pseudomonas aeruginosa and Mycobacterium tuberculosis had monospecific BPI-ANCA (sensitivity 79% and 71%, respectively). UC/PSC and chronic GN bacterial infections caused by Klebsiella pneumoniae, Proteus mirabilis, or Escherichia coli had multispecific BPI-ANCA (sensitivity 64% and 100%, respectively). Odds ratio analysis showed that patients with IBD who were positive for multispecific BPI-ANCA had a 13.5-fold increased risk of UC/PSC (95% CI 2.98–61.18). Conclusions: Monospecific BPI-ANCA may be a valuable biomarker for persistent Pseudomonas aeruginosa and Mycobacterium tuberculosis infections. In contrast, multispecific BPI-ANCA are associated with UC/PSC and persistent infections caused by intestinal Gram-negative bacteria. Suppression of antimicrobial function by multispecific BPI-ANCA could impair the elimination of Gram-negative bacteria, sustaining the immunoinflammation. Dysregulated antimicrobial response might be the target of immunomodulatory therapy in the initial phase of BPI-ANCA-positive UC/PSC. Full article

This paper presents a low-power continuous-time delta-sigma (CT-DS) analog-to-digital converter (ADC) for use in high-sensitivity automotive-grade sub-1 GHz receivers in emerging wireless sensors network applications. The proposed ADC employs a third-order Cascade of Integrators FeedForward and Feedback (CIFF-B) loop filter operating at a sampling frequency of 150 MHz to achieve high energy efficiency and robust noise shaping. A low-noise phase-locked loop (PLL) is integrated to provide high-precision clock signals. The loop filter combines active-RC and GmC integrators with the source degeneration technique to optimize power consumption and linearity. To minimize complexity and enhance stability, a 1-bit quantizer with isolation switches and return-to-zero (RZ) digital-to-analog converters (DACs) are used in the modulator. With a 500 kHz bandwidth, the sensitivity of the receiver is −105.5 dBm. Fabricated in a 180 nm standard CMOS process, the prototype achieves a peak signal-to-noise ratio (SNR) of 76.1 dB and a signal-to-noise and distortion ratio (SNDR) of 75.3 dB, resulting in a Schreier figure of merit (FoM) of 160.7 dB based on SNDR, while consuming only 0.8 mA from a 1.8 V supply. Full article

University students use the Internet regularly for study, socialising, and entertainment; moreover, in adolescents and young adults, Internet use increases with age. More than ever before, the wide availability of online information requires critical thinking coupled with skills for evaluating online information, such as verifying the reliability of information and netiquette. These competencies might influence deep learning; however, few studies have analysed all these variables together. In addition, there is an ongoing academic debate as to whether using smartphones at an early age is beneficial for learning. Our study aimed to analyse, according to the age of the first smartphone, to what extent students’ critical thinking disposition, netiquette, and evaluation of the reliability of online information predict their capacity for deep learning. Our sample comprised 415 Spanish university students aged 18–36 (M = 19.98 and SD = 4.18). The instruments used were, for the assessment of Deep Learning, the Subscale of the questionnaire Attitudes towards learning of university students CEVAPU (to measure the Critical Thinking Disposition, we used the CTDS scale (Spanish adaptation of Bravo et al., 2020 and also the Competence Scale Evaluation of the reliability of online information (e-CEI) (Denoni & Cebollero-Salinas, 2025; and, finally, to assess Netiquette, the subscale of the questionnaire Evaluation of the quality of cyberbehavior “EsCaCiber” Multiple linear regression results indicated that in those participants who indicated they had acquired a smartphone before the age of thirteen, the two competencies of netiquette and evaluation of online information reliability were more strongly predictive of deep learning than in the group of participants who had their first smartphone when they were thirteen or older. Our study confirms that critical thinking disposition is a factor that favours deep learning in both groups (i.e., smartphone acquisition before and after 13 years old). The social and educational implications are along the lines of fostering a disposition to critical thinking, educating in digital literacy, especially in verifying the reliability of information, and communicating with netiquette for deep learning. Our findings indicate a potential association between critical thinking disposition and a greater propensity for deep learning in both groups (i.e., smartphone acquisition before and after 13 years old). A relevant educational implication of the results seems to indicate that a possible way to achieve deep university learning is to encourage critical thinking, to educate in digital literacy, especially in the verification of the reliability of information and to communicate with netiquette. Some of the limitations of the research design are the use of self-reports, convenience sampling and a cross-sectional design. Full article

Chinese coalbed methane (CBM) reservoirs exhibit characteristically low recovery rates due to adsorbed gas dominance and “three-low” properties (low permeability, low pressure, and low saturation). CO₂ thermal drive (CTD) technology addresses this challenge by leveraging dual mechanisms—thermal desorption and displacement to enhance production; however, its effectiveness necessitates uniform fracture networks for temperature field homogeneity—a requirement unmet by conventional long-fracture fracturing. To bridge this gap, a coupled seepage–heat–stress–fracture model was developed, and the temperature field evolution during CTD in coal under non-uniform fracture networks was determined. Integrating multi-cluster fracture propagation with stress barrier and intra-stage stress differential characteristics, a stress-barrier-responsive diverting fracturing technology meeting CTD requirements was established. Results demonstrate that high in situ stress and significant stress differentials induce asymmetric fracture propagation, generating detrimental CO₂ channeling pathways and localized temperature cold islands that drastically reduce CTD efficiency. Further examination of multi-cluster fracture dynamics identifies stress shadow effects and intra-stage stress differentials as primary controlling factors. To overcome these constraints, an innovative fracture network uniformity control technique is proposed, leveraging synergistic interactions between diverting parameters and stress barriers through precise particle size gradation (16–18 mm targeting toe obstruction versus 19–21 mm sealing heel), optimized pumping displacements modulation (6 m³/min enhancing heel efficiency contrasted with 10 m³/min improving toe coverage), and calibrated diverting concentrations (34.6–46.2% ensuring uniform cluster intake). This methodology incorporates dynamic intra-stage adjustments where large-particle/low-rate combinations suppress toe flow in heel-dominant high-stress zones, small-particle/high-rate approaches control heel migration in toe-dominant high-stress zones, and elevated concentrations (57.7–69.2%) activate mid-cluster fractures in central high-stress zones—collectively establishing a tailored framework that facilitates precise flow regulation, enhances thermal conformance, and achieves dual thermal conduction and adsorption displacement objectives for CTD applications. Full article

In this study, we discuss the water masses and their transport in the Chain fracture zone (CFZ), which is a poorly studied part of the Equatorial Atlantic. Our study is based on measurements carried out during the 63rd cruise of R/V “Akademik Ioffe” in 2022. We identified water masses in the CFZ, determined their physical and chemical properties, localized their boundaries and components of the North Atlantic Deep Water (NADW), and calculated the transport of water masses. A four-layer structure of the NADW was identified with two components of middle NADW, which are defined by minimal and maximal oxygen concentrations. The upper boundary of the Antarctic Bottom Water (AABW) corresponds approximately to the isotherm θ = 1.5 °C. The assessed proportion of AABW in the bottom layer at the western entrance to the CFZ is 50%, and not higher than 33% at the eastern exit from the CFZ. For the first time, instrumental observations were carried out at the exit of the CFZ and in its western part. They showed that the AABW flux has an intensity of about 0.02–0.5 Sv depending on the upper boundary of AABW and moves through a passage in the northern wall (at 13° W), and not through the main sill. Full article

Proper estimation of flowing bottomhole pressure at coiled tubing depth (BHP-CTD) is crucial in optimization of nitrogen lifting operations in oil wells. Conventional estimation techniques such as empirical correlations and mechanistic models may be characterized by poor generalizability, low accuracy, and inapplicability in real time. This study overcomes these shortcomings by developing and comparing sixteen machine learning (ML) regression models, such as neural networks and genetic programming-based symbolic regression, in order to predict BHP-CTD with field data collected on 518 oil wells. Operational parameters that were used to train the models included fluid flow rate, gas–oil ratio, coiled tubing depth, and nitrogen rate. The best performance was obtained with the neural network with the L-BFGS optimizer (R² = 0.987) and the low error metrics (RMSE = 0.014, MAE = 0.011). An interpretable equation with R² = 0.94 was also obtained through a symbolic regression model. The robustness of the model was confirmed by both k-fold and random sampling validation, and generalizability was also confirmed using blind validation on data collected on 29 wells not included in the training set. The ML models proved to be more accurate, adaptable, and real-time applicable as compared to empirical correlations such as Hagedorn and Brown, Beggs and Brill, and Orkiszewski. This study does not only provide a cost-efficient alternative to downhole pressure gauges but also adds an interpretable, data-driven framework to increase the efficiency of nitrogen lifting in various operational conditions. Full article

Objectives: Connective tissue diseases (CTDs) include a diverse group of systemic autoimmune conditions, among which interstitial lung disease (ILD) is acknowledged as a major determinant of prognosis. High-resolution computed tomography (HRCT) is the gold standard for ILD assessment. The distribution of HRCT patterns across CTDs remain incompletely defined. The objective of this systematic review is to synthesize available evidence regarding the prevalence of specific radiological patterns within CTD-ILDs and to assess whether specific patterns occur at different frequencies among individual CTDs. Methods: The inclusion criteria encompassed original human studies published in English between 2015 and 2024, involving adult participants (≥18 years) with CTD-ILDs assessed primarily by HRCT and designed as retrospective, prospective, or cross-sectional trials with extractable data. We systematically searched PubMed, Scopus, and Web of Science (January 2025). Risk of bias was evaluated using the Newcastle–Ottawa Scale (NOS) for cohort and case–control studies, and the JBI Critical Appraisal Checklist for cross-sectional studies. Data were extracted and categorized by HRCT pattern for each CTD, and then summarized descriptively and statistically. Results: We analyzed 23 studies published between 2015 and 2024, which included 2020 patients with CTD-ILDs. The analysis revealed non-specific interstitial pneumonia (NSIP) as the most prevalent pattern overall (36.5%), followed by definite usual interstitial pneumonia (UIP) (24.8%), organizing pneumonia (OP) (9.8%) and lymphoid interstitial pneumonia (LIP) (1.25%). HRCT distribution varied by CTD: NSIP predominated in systemic sclerosis, idiopathic inflammatory myopathies, and mixed connective tissue disease; UIP was most frequent in rheumatoid arthritis; LIP was more common in Sjögren’s syndrome. While global differences were statistically significant, pairwise comparisons often lacked significance, likely due to sample size constraints. Discussion: Limitations include varying risk of bias across study designs, heterogeneity in HRCT reporting, small sample sizes, and inconsistent follow-up, which may reduce precision and generalizability. In addition to the quantitative synthesis, this review offers a detailed description of each radiologic pattern mentioned above, illustrated by representative examples to support the recognition in clinical settings. Furthermore, it includes a brief overview of the major CTDs associated with ILD, summarizing their epidemiological data, risk factors for ILD and clinical presentation and diagnostic recommendations. Conclusions: NSIP emerged as the most common HRCT pattern across CTD-ILDs, with UIP predominating in RA. Although inter-disease differences were observed, statistical significance was limited, likely reflecting sample size constraints. These findings emphasize the diagnostic and prognostic relevance of HRCT pattern recognition and highlight the need for larger, standardized studies. Full article

Background: Ulcerative colitis (UC) is a chronic inflammatory disease of the colon with a rising global incidence. Natural conjugated taurocholic acid (TCA) possesses anti-inflammatory properties and shows potential therapeutic effects against UC, although the underlying mechanisms remain unclear. Methods: This study employed an integrative approach—combining network pharmacology, bioinformatics, machine learning, immune infiltration analysis, and molecular docking—to investigate the therapeutic mechanisms of TCA in UC. UC-related gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database, and potential TCA targets were predicted using the Comparative Toxicogenomics Database (CTD) and TargetNet platforms. Differentially expressed genes (DEGs) were identified and analyzed via GO and KEGG enrichment analyses. Results: Four machine learning algorithms (XGBoost, RF, SVM, and NNet) were used to identify six hub genes (TGM2, MMP9, ABCB1, NOS2, ABCG2, CASP1), which were further validated using an artificial neural network. Immune infiltration analysis with CIBERSORT revealed significant alterations in immune cell populations in UC tissues. Further validation through an artificial neural network model confirmed their predictive ability. The enrichment analysis of the hub genes highlighted their roles in immune-related pathways, while the immune infiltration analysis indicated significant differences in immune cell populations between ulcerative colitis tissues and control tissues. The molecular docking results showed that the binding energies of these six proteins to TCA were lower than −5 kcal/mol, with TGM2 having the strongest binding affinity (−10 kcal/mol). The intervention of TCA on colitis mice could improve the inflammatory response by regulating the expression of the TGM2 gene. Conclusions: In conclusion, this study suggests that taurocholate alleviates ulcerative colitis by targeting key genes such as TGM2 and modulating immune-related pathways, providing a novel basis for future therapeutic exploration. Full article

Background: Structurally active phospholipoproteomic formulations that lack pharmacodynamic targets or systemic absorption present unique challenges for validation. Designed for immune compatibility or structural modulation—rather than therapeutic effect—these platforms cannot be evaluated through conventional clinical or molecular frameworks. Methods: This study introduces a standardized, non-invasive ex vivo protocol using real-time kinetic imaging to document biological behavior under neutral conditions. Eight human tumor-derived adherent cell lines were selected for phenotypic stability and imaging compatibility. Phospholipoproteomic preparations were applied under harmonized conditions, and cellular responses were recorded continuously over 48 h. Results: Key parameters included signal continuity, morphological integrity, and inter-batch reproducibility. The system achieved high technical consistency without labeling, endpoint disruption, or destructive assays. Outputs included full kinetic curves and viability signals across multiple cell–fraction pairings. Conclusions: This method provides a regulatorily compatible foundation for functional documentation in non-pharmacodynamic programs where clinical trials are infeasible. It supports early-stage screening, batch comparability, and audit-ready records within SAP, CTD, or real-world evidence (RWE) ecosystems. By decoupling validation from systemic exposure, the protocol enables scalable, technically grounded decision-making for structurally defined immunobiological platforms. Full article

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article

The Centralized Teacher with Decentralized Student (CTDS) framework is a multi-agent reinforcement learning (MARL) approach that utilizes knowledge distillation within the Centralized Training with Decentralized Execution (CTDE) paradigm. In this framework, a teacher module learns optimal Q-values using global observations and distills this knowledge to a student module that operates with only local information. However, CTDS has limitations including inefficient knowledge distillation processes and performance gaps between teacher and student modules. This paper proposes the evolutionary sampling method that employs genetic algorithms to optimize selective knowledge distillation in CTDS frameworks. Our approach utilizes a selective sampling strategy that focuses on samples with large Q-value differences between teacher and student models. The genetic algorithm optimizes adaptive sampling ratios through evolutionary processes, where the chromosome represent sampling ratio sequences. This evolutionary optimization discovers optimal adaptive sampling sequences that minimize teacher–student performance gaps. Experimental validation in the $StarCraft$ Multi-Agent Challenge (SMAC) environment confirms that our method achieved superior performance compared to the existing CTDS methods. This approach addresses the inefficiency in knowledge distillation and performance gap issues while improving overall performance through the genetic algorithm. Full article