Search Results (573)

Reliable prediction of chemical–protein interactions (CPIs) remains a key challenge in drug discovery, especially under sparse or noisy biological data. We present MM-TCoCPIn, a Multi-Modal Topology-aware Chemical–Protein Interaction Network that integrates three causally grounded modalities—network topology, biomedical semantics, and a 3D protein structure—into an interpretable graph learning framework. The model processes topological features via a CTC (Comprehensive Topological Characteristics)-based encoder, literature-derived semantics via SciBERT (Scientific Bidirectional Encoder Representations from Transformers), and structural geometry via a GVP-GNN (Geometric Vector Perceptron Graph Neural Network) applied to AlphaFold2 contact graphs. Evaluation on datasets from STITCH, STRING, and PubMed shows that MM-TCoCPIn achieves state-of-the-art performance (AUC = 0.93, F1 = 0.92), outperforming uni-modal baselines. Importantly, ablation and counterfactual analyses confirm that each modality contributes distinct biological insight: topology ensures robustness, semantics enhance recall, and structure sharpens precision. This framework offers a scalable and causally interpretable solution for CPI modeling, bridging the gap between predictive accuracy and mechanistic understanding. Full article

Background: This study aims to establish standardized reference values for myocardial T1 and T2 relaxation times in a clinically and imaging-defined real-world patient cohort, evaluating their variability in relation to age, sex, and comorbidities. By identifying key physiological and pathological influences, this investigation seeks to enhance CMR-based myocardial mapping for improved differentiation between normal and pathological myocardial conditions. Methods: This retrospective observational study analyzed T1 and T2 relaxation times using CMR at 1.5 Tesla in a cohort of 491 subjects. T1 and T2 times were measured using MOLLI and GRASE sequences, and statistical analyses assessed intra- and interindividual variations, including the influence of age, sex, and comorbidities, to establish reference values and improve myocardial tissue characterization. Results: T1 and T2 relaxation times were analyzed in 291 and 200 participants, respectively. The mean global T1 time was 1004.7 ± 49.8 ms, with no significant differences between age groups (p = 0.81) or sexes (p = 0.58). However, atrial fibrillation (AF) and mitral regurgitation (MR) were associated with significantly prolonged T1 times (p < 0.05). The mean global T2 time was 67.4 ± 8.6 ms, with age-related prolongation (p < 0.05), but no sex differences (p = 0.46). Comorbidities did not significantly influence T2 times, except for NYHA Class III–IV patients, who exhibited prolonged T2 values (p < 0.05). Conclusions: Standardized T1 and T2 reference values are essential to improve diagnostic accuracy and risk stratification in CMR-based myocardial tissue characterization. Future research should focus on multicenter validation, AI-driven analysis, and the development of age- and comorbidity-adjusted normative databases to enhance individualized cardiovascular care. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

This study examines the dynamic interplay between interest rates, inflation, and GDP growth in Southeast Asian economies from 2000 to 2023, employing the Panel ARDL framework with the Pooled Mean Group (PMG) model. The findings confirm a robust long-term relationship among the Deposit Interest Rate (DIR), Lending Interest Rate (LIR), Consumer Price Index (CPI), and GDP growth. Higher deposit rates consistently promote economic expansion by encouraging savings and investment, while lending rates support long-term growth but limit short-term activity due to higher borrowing costs. Inflation adversely affects long-term growth by reducing purchasing power but boosts short-term demand. Historical GDP trends highlight the region’s susceptibility to global shocks, such as the 2008–2010 financial crisis and the 2020 COVID-19 pandemic, with forecasts indicating a gradual recovery from 2021 to 2025. The study emphasizes the importance of balanced monetary policies to enhance growth and stability in Southeast Asia, providing practical insights for policymakers addressing global and regional economic challenges. Full article

Background: Antigen-targeting immunotherapies hinge on the accurate identification of immunogenic epitopes that elicit robust T-cell responses. However, current computational approaches focus primarily on MHC binding affinity, leading to high false-positive rates and limiting the clinical utility of antigen selection methods. Methods: We developed the neoIM (for “neoantigen immunogenicity”) model, a first-in-class, high-precision immunogenicity prediction tool that overcomes these limitations by focusing exclusively on overall CD8 T-cell response rather than MHC binding. neoIM, a random forest classifier, was trained solely on MHC-presented non-self peptides (n = 61.829). Its performance was assessed against that of currently existing alternatives on several in vitro immunogenicity datasets. In addition, its clinical impact was investigated in two retrospective analyses of clinical trial data by assessing the effect of neoIM-based antigen selection on the positive immunogenicity rate of personal vaccine designs. Finally, the potential for neoIM as a biomarker was investigated by assessing the correlation between neoIM scores and overall survival in a melanoma patient cohort treated with checkpoint inhibitors (CPI). Results: neoIM was found to substantially outperform publicly available tools in regards to in vitro benchmarks based on ELISpot assays, with an increase in predictive power of at least 30%, reducing false positives and improving target selection efficiency. In addition, using neoIM scores during patient-specific antigen prioritization and selection was shown to yield up to 50% more clinically actionable antigens for individual patients in two recent clinical trials. Finally, we showed that neoIM could further refine response prediction to checkpoint inhibition therapy, further demonstrating the importance of evaluating neoantigen immunogenicity. Conclusions: These findings establish neoIM as the first computational tool capable of accurately predicting epitope immunogenicity beyond MHC affinity. By enabling more precise target discovery and prioritization, neoIM has the potential to accelerate the development of next-generation antigen-based immunotherapies. Full article

This study investigated the cognitive profiles of Italian university students with dyslexia using the WAIS-IV, comparing them to peers without specific learning disorders. Seventy-one participants took part: 36 with a diagnosis of dyslexia and 35 matched controls. While dyslexic adults showed lower Full Scale IQ (FSIQ) scores compared to controls, their scores remained within the average range. They showed deficits in Working Memory Index (WMI) and Processing Speed Index (PSI) but performed similarly to controls in Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI). Significant group differences also emerged in Arithmetic Reasoning, Symbol Search, and Coding subtests. Logistic regression identified WMI and PSI as the most reliable predictors of dyslexia, showing a good predictive value in discriminating between adults with and without dyslexia. Additionally, dyslexic adults displayed lower Cognitive Proficiency Index (CPI) scores relative to their General Ability Index (GAI), and lower FSIQ scores compared to controls. Overall, dyslexic adults exhibit a distinctive cognitive profile with strengths and weaknesses. This pattern can aid in dyslexia diagnosis, particularly in individuals who have compensated through extensive reading experience in a highly regular orthography. Full article

The geochemistry of the fatty acids in the modern sediments in the Northern South Yellow Sea is still poorly studied, and studies on the geochemistry of the fatty acids in relatively long-core sediment samples are lacking. Thus, the fatty acids in the core sediments in the Northern South Yellow Sea were separated and identified to study their components and distribution characteristics, and the sources of organic matter and the early diagenetic evolution of the fatty acids in the sediments were discussed. The results show that saturated straight-chain fatty acids (methyl ester) have the highest content in the core sediments in the Northern South Yellow Sea, which account for 83.89% of the total fatty acids (methyl ester). nC_16:0 is dominant, accounting for 30.48% of the n-saturated fatty acids (methyl ester). Unsaturated fatty acids (methyl ester) account for 7.59% of the total fatty acids (methyl ester). Binary unsaturated fatty acids (methyl ester) can only be detected in some samples, which are low in content and dominated by C_18:2. Based on the components and distribution of the fatty acids (methyl ester) in the core sediments in the Northern South Yellow Sea, combined with the characteristics of other lipid biomarker compounds, the actual geological background, and previous research results, it is considered that the sources of organic matter in the core sediments are marine–terrestrial mixed materials, with terrestrial materials dominating. The fatty acids’ (methyl ester) CPI, the relative content of short-chain saturated fatty acids (methyl ester), and the unsaturated fatty acids (methyl ester) in the core sediments show non-obvious variation as the burial depth increases, reflecting that the fatty acids in the core sediments are strongly degraded at the early diagenetic stage, and this degradation is controlled by various complicated factors. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

Classical Hodgkin lymphoma (cHL) is a biologically and clinically unique malignancy characterized by rare Hodgkin and Reed–Sternberg (HRS) cells surrounded by a dense and diverse inflammatory infiltrate. These malignant cells actively reshape the tumor microenvironment (TME) through metabolic reprogramming and immune evasion strategies. This review synthesizes current knowledge on how metabolic alterations contribute to tumor survival, immune dysfunction, and therapeutic resistance in cHL. We discuss novel therapeutic approaches aimed at disrupting these processes and examine the potential of combining metabolic interventions with immune-based strategies—such as immune checkpoint inhibitors (CPIs), epigenetic modulators, bispecific antibodies, and CAR-T/CAR-NK cell therapies—which may help overcome resistance and enhance anti-tumor responses. Several agents are currently under investigation for their ability to modulate immune cell metabolism and restore effective immune surveillance. Altogether, targeting metabolic vulnerabilities within both tumor and immune compartments offers a promising, multifaceted strategy to improve clinical outcomes in patients with relapsed or refractory cHL. Full article

Local site conditions can significantly influence the amplitude, duration, and frequency content of seismic recordings, making the characterization of subsoil properties a critical component in seismic hazard assessment. However, despite extensive research, standardized methodologies for assessing site effects are still lacking. This study presents preliminary steps in the site characterization of a small area of Campi Flegrei caldera (Italy), with the aim of enhancing understanding of local lithology and seismic wave propagation. The analysis focuses on the broad-band seismic station CSTH, installed in 2021, and incorporates data from a temporary 2D array of five short-period sensors deployed around the station. These sensors recorded both ambient noise and seismic events associated with caldera dynamics. To improve the robustness of the characterization, data from two additional permanent broad-band stations (CPIS and CSOB) of the Istituto Nazionale di Geofisica e Vulcanologia—Osservatorio Vesuviano’s monitoring network, also located nearby a hydrothermal field, were included. Spectral analyses such as Power Spectral Density (PSD), Horizontal-to-Vertical (H/V) spectral ratios, and f-k array technique were performed to evaluate the frequency-dependent response of the site and to support the development of a comprehensive seismic site model. Full article

The C-kinase-activated protein phosphatase-1 (PP1) inhibitor of 17 kDa (CPI-17) is a specific inhibitor of the PP1 catalytic subunit (PP1c) and the myosin phosphatase (MP) holoenzyme. CPI-17 requires the phosphorylation of Thr38 in the peptide segment ³⁵ARV(P)TVKYDRREL⁴⁶ for inhibitory activity. CPI-17 regulates myosin phosphorylation in smooth muscle contraction and the tumorigenic transformation of several cell lines via the inhibition of MP. A phosphospecific antibody (anti-CPI-17^pThr38) against the phosphorylation peptide was used to determine the phosphorylation levels in cells. We found that phospho-CPI-17 and its closely related proteins are not present in HeLa and MCF7 cells after inducing phosphorylation by inhibiting phosphatases with calyculin A. In contrast, cross-reactions of proteins in the 40–220 kDa range with anti-CPI-17^pThr38 were apparent. Searching the protein database for similarities to the CPI-17 phosphorylation sequence revealed several proteins with 42–75% sequence identities. The LIMK2-1 isoform emerged as a possible PP1 inhibitor. Experiments with Flag-LIMK2-1 overexpressed in tsA201 cells proved that LIMK2-1 interacts with PP1c isoforms and is phosphorylated predominantly by protein kinase C. Phosphorylated LIMK2-1 inhibits PP1c and the MP holoenzyme with similar potencies (IC50 ~28–47 nM). In conclusion, our results suggest that LIMK2-1 is a novel phosphorylation-dependent inhibitor of PP1c and MP and may function as a CPI-17-like phosphatase inhibitor in cells where CPI-17 is present but not phosphorylated upon phosphatase inhibition. Full article

This study analyzes the lacustrine hydrocarbon source rocks of the Lower Cretaceous in the Erdengsumu sag of the Erlian Basin, evaluating their characteristics and identifying areas with oil resource potential, while also investigating the ancient lake environment, material source input, and controlling factors, ultimately developing a sedimentary model for lacustrine hydrocarbon source rocks. The findings suggest the following: (1) The lower Tengger Member (K₁bt₁) and the Aershan Formation (K₁ba) are the primary oil-producing strata, with an effective hydrocarbon source rock exhibiting a lower limit of total organic carbon (TOC) at 0.95%. The Ro value typically remains below 0.8%, indicating that high-maturity oil production has not yet been attained. (2) The oil generation threshold depths for the Dalestai and Sayinhutuge sub-sags are 1500 m and 1214 m, respectively. The thickness of the effective hydrocarbon source rock surpasses 200 m, covering areas of 42.48 km² and 88.71 km², respectively. The cumulative hydrocarbon generation intensity of wells Y1 and Y2 is 486 × 10⁴ t/km² and 26 × 10⁴ t/km², respectively, suggesting that the Dalestai sub-sag possesses considerable petroleum potential. The Aershan Formation in the Chagantala sub-sag has a maximum burial depth of merely 1800 m, insufficient to attain the oil generation threshold depth. (3) The research area’s productive hydrocarbon source rocks consist of organic matter types I and II₁. The Pr/Ph range is extensive (0.33–2.07), signifying a reducing to slightly oxidizing sedimentary environment. This aligns with the attributes of small fault lake basins, characterized by shallow water and robust hydrodynamics. (4) The low ratio of ∑nC₂₁₋/∑nC₂₂₊ (0.36–0.81), high CPI values (>1.49), and high C₂₉ sterane concentration suggest a substantial terrestrial contribution, with negligible input from aquatic algae–bacterial organic matter. Moreover, as sedimentation duration extends, the contribution from higher plants progressively increases. (5) The ratio of the width of the deep depression zone to the width of the depression in the Erdengsumu sag is less than 0.25. The boundary fault scale is small, its activity is low, and there is not much input from the ground. Most of the source rocks are in the reducing sedimentary environment of the near-lying gently sloping zone. Full article

Preterm infants often require oxygen supplementation, resulting in high risk for bronchopulmonary dysplasia (BPD) and neurodevelopmental deficits. Despite a growing number of studies, there is still little knowledge about brain injury in BPD models. Therefore, we exposed neonatal C57BL/6 mice to 85% oxygen from birth to postnatal day (P) 14. At P28, two weeks after recovery under normoxic conditions, right hemisphere was used for the analysis of mRNA and the left hemisphere for protein expression of neuronal cells, neuroinflammatory and vascularisation markers, analysed by real-time PCR and Western blot, respectively. Hyperoxia led to an altered expression of markers associated with neuronal and oligodendrocyte maturation and neuroinflammation such as Dcx, Nestin, Il-1β, Il-6, NG2, and YM1/2. These changes were accompanied by an increased expression of genes involved in angiogenesis and vascular remodelling, e.g., Vegf-a, Nrp-1, and Icam-1. Together, 14 days of hyperoxia triggered a phenotypic response, resembling signs of encephalopathy of prematurity (EoP). Full article

Background: This study aimed to evaluate the effectiveness of a saliva-based screening method for periodontal disease among community-dwelling older adults in Japan. Methods: A total of 372 study participants (mean age: 73.1 years) with 20 or more remaining teeth were included in the study. Of the six parameters assessed by the Salivary Multi Test (SMT), this study focused on the three parameters related to periodontal disease: occult blood, leukocytes, and proteins. Periodontal tissue examinations were performed based on the Community Periodontal Index (CPI) using partial mouth recording. To evaluate screening accuracy, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each of the three markers: occult blood, leukocytes, and proteins. Receiver operating characteristic (ROC) analysis was performed for each SMT item, and area under the curve (AUC) was calculated. Logistic regression analysis was used to calculate the odds ratios for combinations of SMT markers, with the presence of periodontal pockets and gingival inflammation as the respective outcome variables. Results: Among the individual markers, occult blood showed the highest diagnostic performance for detecting both periodontal pockets and gingival inflammation. The combination of elevated occult blood and leukocyte levels yielded the highest odds ratios for both periodontal pockets and gingival inflammation. Conclusions: While several SMT markers showed associations with periodontal conditions, their utility for screening in older Japanese adults remains to be further validated. Combining markers may help improve diagnostic performance, but additional studies are warranted. Full article

Apple pomace, a by-product from the production of concentrated juice, is a major contributor to global food waste. Despite its beneficial nutritional profile, apple pomace is predominantly disposed of in landfills. Rapid fermentation and spoilage caused by microorganisms are compounding factors in this demise, despite significant research into upcycling strategies. Thus, there is an unmet need for economical approaches that allow for the preservation of pomace during storage and transportation to centralized processing facilities from regional hubs. To address this challenge, we investigated the potential of different preservatives for preventing microbial growth and the spoilage of apple pomace, including antimicrobials (natamycin and iodine), polysaccharides (chitosan and fucoidan), and acetic acid. Spread plates for total microbial and fungal counts were employed to assess the effectiveness of the treatments. High concentrations (10,000 ppm) of chitosan were effective at reducing the microbial load and inhibiting growth, and in combination with antimicrobials, eliminated all microbes below detectable levels. Nevertheless, acetic acid at an equivalent concentration to commercial vinegar displayed the highest economic potential. Apple pomace submerged in 0.8 M acetic acid (3 kg pomace per liter) resulted in a five-log reduction in the microbial colony-forming units (CFUs) out to 14 days and prevented fermentation and ethanol production. These results provide a foundation for the short-term storage and preservation of apple pomace that could contribute to its upcycling. Full article