Search Results (196)

Background/Objectives: Systemic inflammation and nutritional status are two factors known to influence the prognosis of cancer patients. The Naples Prognostic Score (NPS) includes two inflammatory and two nutritional markers and combines them in a single score to better predict survival of cancer patients. In this study, we aimed to critically evaluate the NPS for its significance in head and neck cancer patients. Methods: We retrospectively analyzed the preoperative NPS and its association with overall survival (OS) and disease-free survival (DFS). We evaluated 140 patients with head and neck squamous cell carcinoma (HNSCC) who were treated with primary surgical therapy at a tertiary center between 2001 and 2019. OS and DFS were analyzed using Kaplan–Meier estimators, the log-rank test and uni- and multivariable Cox models. Results: The median postoperative follow-up was 6.7 years. Higher NPS showed numerically, but not significantly shorter OS and DFS. Sensitivity analysis for all markers included in the NPS revealed only the neutrophil-to-lymphocyte ratio (NLR) as a significant predictor for OS and DFS. Conclusions: These findings suggest that the overall NPS may have limited prognostic value in this cohort. In contrast, NLR appears to be a more robust and clinically relevant marker for survival outcomes in patients with head and neck squamous cell carcinoma. Full article

Background/Objectives: Ceftolozane/tazobactam (C/T) and ceftazidime/avibactam (CZA) are critical therapeutic options for multidrug-resistant Gram-negative infections; however, their comparative pharmacodynamics against carbapenem-resistant Pseudomonas aeruginosa (CRPA) remain incompletely defined. This study aimed to compare the bactericidal activity of C/T and CZA administered by intermittent infusion (II) or continuous infusion (CI) using a hollow fiber infection model (HFIM) against a clinical isolate of CRPA. Methods: Clinically relevant concentration–time profiles for C/T and CZA based on prescribing information were simulated in the HFIM. The standard P. aeruginosa strain ATCC 27853 and a GES-producing clinical CRPA isolate were utilized. The primary endpoint was bactericidal activity (≥3 log₁₀ CFU/mL reduction from baseline), while secondary endpoints included regrowth prevention and resistance development based on population analysis profiles (PAPs). Results: Against the standard strain, both agents achieved rapid killing without regrowth. However, for the GES-producing clinical isolate, C/T failed to achieve bactericidal activity. In contrast, CZA demonstrated sustained bacterial killing activity with the most pronounced early-phase bactericidal activity with CI of CZA (−4.25 log₁₀ CFU/mL at 24 h). The bactericidal activity was persistent over 7 days without bacterial regrowth after treatment discontinuation. Conversely, bacterial regrowth occurred with II of CZA after drug withdrawal. PAPs showed the lack of resistance development against CZA, whereas resistance to C/T developed within 48 h after initiating therapy. Conclusions: In this HFIM study, CI of CZA demonstrated the most sustained suppression of bacterial growth and prevented resistance emergence against the tested clinical isolate of CRPA producing GES β-lactamases. Future clinical studies are warranted to assess the effectiveness of the CI regimen. Full article

This study investigates whether Responsible Property Investing (RPI) attributes are capitalized into condominium prices in the Bangkok Metropolitan Region. An integrated analytical framework combining Exploratory Factor Analysis (EFA) and a log–log Hedonic Price Model (HPM) was applied to a dataset of 187 condominium units derived from Environmental Impact Assessment (EIA) reports and market data. The results indicate that traditional determinants remain dominant. Unit characteristics, particularly spatial quality (β = 0.530) and interior decoration (β = 0.244), exhibit the strongest positive effects, while building amenities also contribute positively (β = 0.260). In contrast, building density (β = −0.168) and location-related distances, including transport accessibility (β = −0.323), negatively affect prices. Most RPI-related attributes are not statistically significant. Only sustainable technology (R4) shows a significant but negative effect (β = −0.206), reflecting heterogeneous valuation. These findings suggest that sustainability features are valued primarily when their benefits are directly observable, while other attributes remain weakly perceived due to information asymmetry and delayed economic returns. Overall, sustainability is only partially capitalized and context-specific in this emerging market, highlighting the need for improved market signaling, policy incentives, and greater transparency of performance information to enhance value recognition. Full article

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty shock is proxied by the U.S. presidential election of 5 November 2024, while the action shock is proxied by President Trump’s 2 April 2025 announcement of reciprocal tariffs. Using daily log returns for the S&P 500 and leading indices for Canada, France, Germany, Italy, Japan and the United Kingdom, we cover January 2010 to July 2025 and assess event effects using correlation paths for June 2024–June 2025 and symmetric ±30-day windows. We employ a DCC-GARCH model to jointly estimate conditional variances and dynamic correlations for six USA-G7 pairs. The results indicate persistent correlation dynamics, with Canada/USA the highest and Japan/USA the lowest. Election-related uncertainty is associated with declines in correlation for European pairs, suggesting temporary decoupling, while Canada and Japan show only small changes. By contrast, the tariff action shock significantly increases conditional correlations across all country/USA pairs, implying stronger market synchronisation, with the largest increases in North America and parts of Europe, and the smallest adjustment in Japan. Full article

Fix $m\ge 3$. The choice number $ch\left(K_{m,n}\right)$ of the complete bipartite graph $K_{m,n}$ has two sharp thresholds as n grows. We give complete proofs of the Hoffman–Johnson values at levels $m+1$ and m, and we pin down the extremal list assignments at the lower threshold ${\displaystyle n_0={(m-1)}^{m-1}-{(m-2)}^{m-1}}$. Specifically, $ch\left(K_{m,n}\right)=\left\{\begin{array}{cc}m+1,\hfill & n\ge m^m,\hfill \\ m,\hfill & n_0\le n<m^m,\hfill \\ \le m-1,\hfill & n<n_0.\hfill \end{array}\right.$ Our method centers on a transversal obstruction principle and a dichotomy for how the M-side lists can intersect when all lists have size $m-1$: Case I, in which some $m-1$ of the M-lists are pairwise disjoint, and Case II, in which three M-lists pairwise intersect with all remaining lists mutually disjoint. For $m\ge 5$ we show that the three-way intersection pattern (three pairwise intersecting M-lists) is strictly non-extremal, and we prove the uniqueness of extremal configurations: we classify all uniformly critical assignments at $n_0$ and show that, up to relabeling, there are exactly two extremal types for $m\ge 5$, while a third type appears for $m\in \{3,4\}$. Finally, we propose a fixed-k block model for deeper levels $ch\left(K_{m,n}\right)=m-k+1$ and contrast this unbalanced setting with the balanced case $m=n$, where $ch\left(K_{m,m}\right)\sim {log}_{2}m$, highlighting the shift from polynomial to logarithmic threshold growth. Full article

Log anomaly detection in modern distributed systems is challenging. Anomalous behaviors are rare. Manual labeling is expensive. Session boundaries are often set by fixed heuristics before model training. This fixed-boundary assumption is problematic because segmentation errors propagate into representation learning and cannot be corrected during optimization. To address this, this paper proposes BASN (Boundary-Aware Sessionization Network), a boundary-aware contrastive learning framework that jointly learns session boundaries and anomaly representations using a differentiable soft-reset mechanism. BASN does not treat sessionization as a separate step. Instead, it predicts boundary probabilities from event semantics and temporal gaps, then modulates end-to-end session-state updates. The session representations are optimized with self-supervised contrastive learning, enabling effective zero-shot anomaly detection and few-shot adaptation. Experiments on four benchmark datasets (BGL, HDFS, OpenStack, SSH) show strong zero-shot performance (area under the receiver operating characteristic curve, AUROC 0.935–0.975) and boundary alignment with expert-validated proxy segmentation (boundary F1 0.825–0.877). Comparative gains over baselines are reported in the article after bibliography correction, baseline verification, and expanded statistical analysis. BASN is also computationally efficient, requiring less than 10 ms per session on a Graphics Processing Unit (GPU) and less than 45 ms on a Central Processing Unit (CPU). This is compatible with real-time inference needs in the evaluated settings. However, cross-system transfer AUROC (0.735–0.812) remains below in-domain performance. Domain-specific adaptation is still needed for deployment in environments that differ greatly from the training domain. Full article

Background: Persistent endodontic infections involving Enterococcus faecalis and Candida albicans are a major cause of root canal treatment failure. Although conventional irrigants, such as sodium hypochlorite and chlorhexidine (CHX), exhibit strong immediate antimicrobial activity, microbes may survive and recover from the initial antimicrobial effect, hence limiting their effectiveness, especially in complex root canal anatomies and in the apical terminus of the tooth. Antibacterial dressing techniques were not proven satisfactory due to depletion of the antibacterial component or difficulty in spreading it evenly along the entire root canal. This study aimed to develop and evaluate the antimicrobial efficacy and release characteristics of a novel sustained-release device (SRD), delivering CHX via gutta-percha points coated with a sustained-release formulation used as a temporary intracanal medicament. Methods: Gutta-percha points were coated with two sustained-release CHX varnishes (CHX1 and CHX2) or a placebo and assessed in vitro. Antimicrobial activity against E. faecalis and C. albicans was evaluated using agar diffusion assays over time. Release kinetics were analyzed using Rhodamine-labeled SRD in a 3D-printed acrylic molar tooth model via fluorescence microscopy. Additionally, biofilm-infected acrylic molar teeth were treated with a placebo, a single 2% CHX irrigation, or SRD-coated gutta-percha points placed as an intracanal dressing prior to obturation. Microbial viability was quantified by colony-forming unit (CFU/mL) analysis from root canals and gutta-percha points. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc multiple comparison test (p < 0.05). Results: SRD-coated gutta-percha points demonstrated sustained antimicrobial activity for up to 21 days against E. faecalis and 19 days against C. albicans. Fluorescence analysis, in an acrylic tooth model, confirmed continuous release for up to 15 days, with pronounced diffusion in the isthmus and palatal canals. In biofilm-infected acrylic teeth models, SRD treatment resulted in a significant reduction of 2–3 log₁₀ CFU/mL compared to placebo groups (p < 0.001) and prevented microbial rebound over the 14-day observation period. In contrast, a single application of 2% CHX solution showed only transient reduction followed by regrowth. Conclusions: Sustained-release CHX delivery via polymer-coated gutta-percha points provided prolonged antimicrobial activity against bacterial and fungal biofilms compared to conventional single-dose CHX application in this in vitro model. These findings support the potential use of coated gutta-percha points as a removable intracanal drug delivery platform prior to final obturation, although further studies incorporating direct-release quantification and in vivo validation are required before clinical translation. Full article

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for “definite” or “probable” infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics–CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted. Full article

Background: Extracellular vesicles (EVs) are increasingly explored as nanocarriers in drug delivery; however, selecting an appropriate loading strategy for a given small-molecule cargo still relies largely on empirical, resource-intensive parallel screening within EV formulation workflows. Despite the widespread application of passive incubation, electroporation, saponin-mediated permeabilization, freeze–thaw cycling, and sonication, there is currently no mechanistically grounded, descriptor-informed framework that enables rational prioritization of loading methods during the early design stage of EV-based dosage forms, leading to inefficient trial-and-error experimentation. Methods: We assembled a chemically diverse dataset of 21 compounds with experimentally determined loading efficiencies across five EV loading methods and calculated seven mechanistically motivated physicochemical descriptors (LogP, molecular weight, aqueous solubility, hydrogen bond donors/acceptors, polar surface area, and formal charge) for each drug. Separate Elastic Net regression models were trained for each loading strategy. Model performance was evaluated using leave-one-out cross-validation, a predefined external validation set (n = 4), and 50 repeated random train–test splits. The analysis emphasized decision-level ranking of loading methods rather than the precise prediction of absolute efficiencies. The applicability domain was assessed via leverage analysis to define the supported chemical space for prospective implementation in EV-based formulation development. Results: As anticipated for biologically heterogeneous EV systems, continuous regression performance remained modest (LOOCV R² = 0.06–0.41). In contrast, decision-level accuracy for identifying the experimentally optimal loading method was consistently high across validation schemes (internal: 76.5%; predefined external: 75%; repeated random validation: 80.5 ± 16.8%). Mechanical disruption methods (freeze–thaw and sonication) demonstrated comparatively greater predictive stability, while misclassification patterns suggested potential nonlinear behavior for highly polar, ionizable cargos. All compounds resided within the leverage-defined applicability domain, confirming adequate descriptor-space representation. Conclusions: This study establishes a mechanistically interpretable, descriptor-based decision-support framework capable of reliably prioritizing EV loading strategies for small-molecule cargos beyond empirical chance without altering standard protocols. By reframing the modeling objective from high-precision efficiency prediction to robust ranking of candidate methods, the approach offers a practical tool to triage between commonly used techniques, thereby reducing experimental burden in early-stage EV formulation development. The framework provides a quantitative basis for integrating molecular-descriptor-guided method selection into rational EV-based drug delivery design and can be expanded with membrane-specific descriptors and larger datasets. Full article

Background/Objectives: To evaluate the immunohistochemical expression of hexokinase-2 (HK2), glutaminase-1 (GLS1), and fatty acid synthase (FASN) and its prognostic significance in diffuse gastric adenocarcinoma. Materials and Methods: Formalin-fixed paraffin-embedded tissue samples from 92 patients with diffuse gastric adenocarcinoma were analyzed. Immunohistochemistry (IHC) was performed to assess the expression of HK2, GLS1 and FASN. Expression levels were evaluated semi-quantitatively based on staining intensity and the percentage of positive cells. Associations between enzyme expression and clinicopathological features were assessed using the Chi-square test. Kaplan–Meier survival analysis was employed to evaluate progression-free survival (PFS) and overall survival (OS) and the log-rank test and Cox proportional hazards models were used for statistical analysis. Results: HK2 and FASN were overexpressed in 20.7% and 22.8% of patients, respectively, and were significantly associated with advanced tumor stage. In contrast, GLS1 expression, found in 30.4% of patients, did not independently correlate with clinicopathological characteristics. Furthermore, HK2 expression and co-expression of HK2/FASN (10.9%) and HK2/GLS1/FASN (8.7%) were associated with progressive disease. In the univariate analysis, stage, HK2 overexpression, and co-expression of HK2/FASN and HK2/GLS1/FASN were associated with shorter survival. However, only stage retained prognostic value in the multivariate analysis. Conclusions: Co-expression of these key metabolic enzymes remains a promising candidate as prognostic markers and therapeutic targets. Concurrent targeting of these metabolic pathways may offer novel therapeutic opportunities for patients with advanced-stage gastric cancer. Full article

Background: Accurate perioperative cardiovascular risk stratification remains challenging in patients undergoing noncardiac surgery. Although treadmill testing (TMT) is widely used for functional assessment, its ability to identify truly high-risk patients is limited. Natriuretic peptides reflect integrated myocardial stress and may provide complementary prognostic information, particularly in patients with abnormal functional test results. Methods: In this prospective multicenter observational study, 178 patients with at least one Revised Cardiac Risk Index risk factor undergoing noncardiac surgery were included. All patients underwent preoperative TMT and had available N-terminal pro–B-type natriuretic peptide (NT-proBNP) measurements. The primary endpoint was 30-day major adverse cardiac events (MACE), defined as a composite of cardiac death, nonfatal myocardial infarction, myocardial injury after noncardiac surgery, pulmonary edema with heart failure, and clinically significant arrhythmias. Incremental prognostic value was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI), with internal validation using bootstrap resampling. Results: At 30 days, 26 patients (14.6%) experienced MACE, of whom seven experienced more than one event. Log-transformed NT-proBNP was independently associated with perioperative events in parsimonious multivariable models. Elevated NT-proBNP, particularly NT-proBNP ≥ 1000 pg/mL, was independently associated with perioperative events after multivariable adjustment. Importantly, the incremental prognostic value of NT-proBNP was most pronounced in patients with a positive TMT, in whom NT-proBNP improved risk discrimination (ΔAUC = +0.09) and reclassification (NRI = 1.00). In contrast, among patients with a negative TMT, the additional prognostic contribution of NT-proBNP was modest and not statistically significant. Subgroup findings should be interpreted cautiously, given the limited number of events. Conclusions: Preoperative NT-proBNP provides modest but independent incremental prognostic value beyond treadmill testing, with the greatest impact observed in patients with positive TMT results. Although improvements in discrimination were moderate, NT-proBNP may help refine perioperative risk assessment in selected intermediate- to high-risk patients. These findings support a complementary biomarker-based approach to MACE. Full article

In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO₂ trajectories in the EU-27 during 2000–2022 using a harmonized annual panel built primarily from the European Commission’s Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO₂ emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO₂ emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver. Full article

To investigate SARS-CoV-2 antibody kinetics and household transmission, infected children along with their families were tested for anti-nucleocapsid antibodies at 1, 3, 6, 9 and 12 months post-SARS-CoV-2 infection during the Ancestral, Alpha, Delta, and Omicron waves. We prospectively included SARS-CoV-2 acute infected children (n = 189). After household recruitment (n = 76 households), the total study population was 228 children and 105 adults. The median age (IQR) of children and adults was 96 (115) months and 504 (96) months, respectively. Anti-nucleocapsid (anti-N) COI (cut-off index) titers peaked at three months post-infection and declined thereafter (p-value < 0.001), and 89.2% remained seropositive at 12 months. Children displayed significantly higher anti-N COI titers than adults during the Delta (p-value: 0.018) and Omicron (p-value: 0.047) periods. Household contact anti-N positivity (evidence of infection) was associated with pediatric index cases (aOR: 1.61, 95% CI: 1.11–2.35; p-value: 0.013) and elevated early anti-N COI titers (aOR: 1.24 per log₁₀ unit, 95% CI: 1.05–1.48; p-value: 0.011). Higher secondary attacks were detected in Delta (aOR: 2.12, 95% CI: 1.19–3.77; p-value: 0.011) and Omicron (aOR: 2.75, 95% CI: 1.44–5.25; p-value: 0.002) compared to Ancestral. Waning of SARS-CoV-2 anti-N titers was faster in secondary cases (aHR: 1.62, 95% CI: 1.01–2.59; p-value: 0.047, Cox model) and during Omicron infection (aHR: 1.74 vs. Ancestral, 95% CI: 1.08–2.79; p-value: 0.023). In contrast, waning was slower in SARS-CoV-2 cases with higher baseline anti-N COI titers (aHR: 0.77, 95% CI: 0.64–0.93; p-value: 0.011). These findings demonstrate variant-specific, age-dependent antibody kinetics, emphasizing that pediatric index cases were associated with higher odds of household infection. Full article

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization—including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)—was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes—Cluster-1 (metabolic–quiescent), Cluster-2 (transition–intermediate), and Cluster-3 (proliferation–inflammatory)—which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators—including ALAS1, NONO, and CSNK1D—in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies. Full article

Permeability prediction of deep metamorphic rock reservoirs in the southwestern Bohai Bay Basin poses an enormous challenge due to the strong heterogeneity. Fractures widely develop in such reservoirs, yet their contributions to permeability were neglected in traditional prediction models. To develop an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19–6 Region—a typical deep metamorphic rock reservoir in the southwestern Bohai Bay Basin—as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and the correlations between the shape of porosity frequency spectrum and rock pore structure were analyzed. Afterwards, two parameters, which were defined as the logarithmic mean (φ_gm) and standard deviation between two golden section points (φ_gsr), were extracted to reflect the main peak position and wide porosity frequency spectrum, and a novel permeability prediction model was established. After the target formations were classified into two types according to the differences in pore types and pore–fracture configuration relationships, the model coefficients were calibrated. Consecutive permeability curves were derived from the proposed model in the intervals where porosity frequency spectra were obtained. Comparisons of predicted permeabilities from the proposed model, traditional method and core-measured results showed that the proposed model yielded far more reliable results, with an average relative error of only 11.12% between the predicted and core-measured permeabilities. In contrast, the average relative error of the traditional method reached 36.10%. The proposed model contributed significantly to the characterization and effectiveness evaluation of fractured deep metamorphic rock reservoirs. Full article