Search Results (464)

Background and Objectives: Type I interferon (IFN-I) transcriptional signatures are widely utilised as readouts of innate immunity. We evaluated whether age and sex affect single interferon-stimulated genes (ISGs) and the composite IFN-I score, with implications for control selection and assay calibration. Materials and Methods: Ninety-five healthy individuals (53 males, 42 females; 18 days to 89 years) were studied. Whole-blood expressions of IFI27, IFI44L, IFIT1, ISG15, RSAD2 and SIGLEC1 was quantified by RT-qPCR, normalised to GAPDH and calibrated to a paediatric reference. Age associations used Spearman’s rho; sex differences, two-sided Mann–Whitney U tests. Results: Age effects were modest and gene-specific: IFI44L declined and IFI27 increased with age (significant overall and in females), whereas in males only IFI44L decreased; other ISGs were null (|r| ≤ 0.36). The composite IFN-I score showed no association with age or sex, indicating that aggregation mitigates small gene-level variation and that demographic influences on baseline IFN-I readouts appear minimal within this six-gene whole-blood qPCR panel in our cohort. Conclusions: Methodologically, a single primary cut-off within homogeneous pipelines is appropriate. Although best practice favours age-, sex- and matrix-matched healthy controls, our data show no significant age- or sex-related differences in the composite IFN-I score; matching therefore primarily supports comparability and clinical governance rather than correction of demographic shifts. Full article

To address the high computational and communication overheads and the limited edge security found in many existing batch verification methods for Mobile Edge Computing (MEC), this paper presents a blockchain-based batch authentication and symmetric group key agreement protocol. A core feature of this protocol is the establishment of a shared symmetric key among all authenticated participants. This symmetry in key distribution is fundamental for enabling secure and efficient broadcast or multicast communication within the MEC group. The protocol introduces a chameleon hash function built on elliptic curves, allowing smart mobile devices (SMDs) to generate lightweight signatures. The edge server (ES) then performs efficient large-scale batch authentication using an aggregate signature technique. Considering the need for secure and independent communication between SMDs and ES, the protocol further establishes a one-to-one session key agreement mechanism and uses a Merkle tree to verify session key correctness. Formal verification with ProVerif2.05 tool confirms the protocol’s security and multiple protection properties. Experimental results show that, compared with the CPPBA, ECCAS, and LBVP schemes, the protocol improves computational efficiency of batch authentication by 0.94%, 67.20%, and 49.53%, respectively. For group key agreement, the protocol achieves a 35.26% improvement in computational efficiency over existing schemes. Full article

Single photon LiDAR has demonstrated remarkable proficiency in long-range sensing under conditions of weak returns. However, in the few-photon regime (SPPP ≈ 1) and at low signal-to-background ratios (SBR ≤ 0.1), depth estimation is subject to significant degradation due to Poisson fluctuations and background contamination. To address these challenges, we propose GLARE-Depth, a patch-wise Poisson-GLRT framework with reflectance-guided spatial fusion. In the temporal domain, our method employs a continuous-time Poisson-GLRT peak search with a physically consistent exponentially modified Gaussian (EMG) kernel, complemented by closed-form amplitude updates and mode-bias correction. In the spatial domain, we implement a methodology that incorporates reflectance-guided, edge-preserving aggregation and confidence-gated lightweight hole filling to enhance effective coverage for few-photon pixels. In controlled simulations derived from the Middlebury dataset, under high-background conditions (SPPP ≈ 1, SBR ≈ 0.06–0.10), GLARE-Depth demonstrates substantial gains over representative baselines in RMSE, MAE, and valid-pixel ratio (insert concrete numbers when finalized) while maintaining smoothness in planar regions and sharpness at geometric boundaries. These results highlight the robustness of GLARE-Depth and its practical potential for low-SBR scenarios. Full article

Preventable medication harm in oncology is often driven by drug-related adverse events (AEs) that trigger order changes such as holds, dose reductions, delays, rechallenges, and enhanced monitoring. Much of the evidence needed to make these decisions lives in unstructured clinical texts, where large language models (LLMs), a type of artificial intelligence (AI), now offer extraction and reasoning capabilities. In this narrative review, we synthesize empirical studies evaluating LLMs and related NLP systems applied to clinical text for oncology AEs, focusing on three decision-linked tasks: (i) AE detection from clinical documentation, (ii) Common Terminology Criteria for Adverse Events (CTCAE) grade assignment, and (iii) grade-aligned actions. We also consider how these findings can inform pharmacist-facing recommendations for order-level safety. We conducted a narrative review of English-language studies indexed in PubMed, Ovid MEDLINE, and Embase. Eligible studies used LLMs on clinical narratives and/or authoritative guidance as model inputs or reference standards; non-text modalities and non-empirical articles were excluded. Nineteen studies met inclusion criteria. LLMs showed the potential to detect oncology AEs from routine notes and often outperformed diagnosis codes for surveillance and cohort construction. CTCAE grading was feasible but less stable than detection; performance improved when outputs were constrained to CTCAE terms/grades, temporally anchored, and aggregated at the patient level. Direct evaluation of grade-aligned actions was uncommon; most studies reported proxies (e.g., steroid initiation or drug discontinuation) rather than formal grade-to-action correctness. While prospective, real-world impact reporting remained sparse, several studies quantified scale advantages and time savings, supporting an initial role as high-recall triage with pharmacist adjudication. Overall, the evidence supports near-term, pharmacist-in-the-loop use of AI for AE surveillance and review, with CTCAE-structured, citation-backed outputs delivered into the pharmacist’s electronic health record order-verification workspace as reviewable artifacts. Future work must standardize reporting and CTCAE/version usage, and measure grade-to-action correctness prospectively, to advance toward order-level decision support. Full article

Background: Validated instruments can be used to quantify vaccine hesitancy, but few provide transportable operational cut-offs or temporal stability metrics. We evaluated the Oxford Vaccine Hesitancy Scale (OVHS) to quantify discrimination for the self-reported historical COVID-19 vaccine refusal, derive and validate a single operational cut-off across populations, and assess the test–retest reliability. Methods: Five datasets (including one from a repeat administration) comprising 2451 assessments from 1989 demographically representative UK and US respondents were analyzed without pooling. Receiver operating characteristic (ROC) curves with 10,000 bootstrap replications (bias-corrected and accelerated and percentile Cis) were used to quantify discrimination. A Youden’s J near-optimal plateau algorithm constrained by a cross-dataset specificity floor (≥0.75) was used to select a transportable cut-off. A prevalence-agnostic aggregate Index of Union (Iuagg) provided secondary confirmation of this cut-off. Cut-off behaviour was visualized with a multi-sample two-graph ROC plot. The six-week test–retest reliability on a UK sample used a two-way mixed-effects, absolute-agreement ICC(A,1). Results: AUCs ranged 0.760–0.971 across datasets (derivation AUC: 0.960). The Youden plateau spanned scores 34–38; applying the specificity floor yielded an operational cut-off OVHS ≥ 35, which was confirmed by the Iuagg. At ≥35, sensitivity/specificity were 0.73–0.95/0.63–0.87 across samples; negative predictive value exceeded 0.90 in all cohorts. The test–retest reliability was good to excellent, with the OVHS total ICC(A,1) = 0.884 (95% CI 0.863–0.903); subscales ranged from 0.649 to 0.901 and the average-measure ICC(A,2) = 0.939. Conclusions: The OVHS demonstrates good-to-excellent discrimination for historical COVID-19 vaccine refusal and strong temporal stability. We found a single, transparent, and transportable operational cut-off (≥35). Our cut-off derivation framework is scale- and endpoint-agnostic and may generalize to other hesitancy instruments and decision cut-offs more broadly. Full article

Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX), Random Forest (RF), and Long Short-Term Memory (LSTM) models, unified through a residual-correction mechanism to capture both linear seasonal and nonlinear temporal dynamics. The framework performs fine-grained 5 min forecasting at both appliance and aggregate levels, revealing that the aggregate forecast achieves higher stability and accuracy than the sum of appliance-level predictions. To ensure operational resilience, three independent hybrid models are deployed across distinct cloud platforms with a two-out-of-three voting scheme, that guarantees continuity if a single-cloud interruption occurs. Using a real residential dataset from a house in Summerlin, Las Vegas (2022), the proposed system achieved a Root Mean Squared Logarithmic Error (RMSLE) of 0.0431 for aggregated load prediction representing a 35% improvement over the next-best model (Random Forest) and maintained consistent prediction accuracy during simulated cloud outages. These results demonstrate that the proposed framework provides a scalable, fault-tolerant, and accurate energy forecasting. Full article

Nascent polypeptides selected for export are synthesized in the cytoplasm by ribosomes and inserted into or translocated across membranes to reach their correct location. Exported proteins delay their folding and remain soluble during their cytoplasmic transit to the membrane. In bacteria, most secretory proteins require additional support from cytosolic chaperones such as trigger factor (TF) and SecB to promote their translocation competence. Here, we investigate the effect of TF and SecB on the folding dynamics and in vitro translocation of secretory and cytoplasmic model proteins PpiA and PpiB, respectively. Global hydrogen—deuterium exchange mass spectrometry (HDX-MS) experiments reveal that SecB delays the folding of slow-folding PpiA proteins but has no effect on fast folders like PpiB. In vitro protein translocation results show that TF inhibits the Sec-dependent translocation of mature PpiA/B and derivative proteins, as well as some secretory preproteins carrying a signal peptide (SP), whereas SecB has no clear effect under the same conditions. However, SecB proves to be dominant over TF in protein translocation in vitro. Finally, for the secretory preprotein proPpiA, SecB prevents SP-induced aggregation. Our findings indicate that the combined properties of signal peptides and mature domains dictate chaperone specificity and translocation efficiency, with both TF and SecB acting in a substrate-specific manner. Full article

Numerous experimental and numerical studies have extensively investigated the performance of reinforced deep beams made with natural coarse aggregate concrete. However, limited research has been carried out on reinforced deep beams made of concrete with coarse aggregate from recycled materials and steel fibers. The main goal of this research is to create an accurate finite element model that can mimic the behavior of deep beams using concrete with recycled coarse aggregate and different ratios of steel fibers. The suggested model represents the pre-peak, post-peak, confinement, and concrete-to-steel fiber bond behavior of steel fiber concrete, reinforcing steel, and loading plates by incorporating the proper structural components and constitutive laws. The deep beams’ nonlinear load–deformation behavior is simulated in displacement-controlled settings. In order to verify the model’s correctness, the ultimate loading capacity, load–deflection relationships, and failure mechanisms are compared between numerical predictions and experimental findings. The comparison outcomes of the performance of the beams demonstrate that the numerical model effectively predicts the behavior of deep beams constructed with recycled coarse aggregate concrete. The findings of the experiment and the numerical analysis exhibit a high degree of convergence, affirming the model’s capability to accurately simulate the performance of such beams. In light of how accurately the numerical predictions match the experimental results, an extensive parametric study is conducted to examine the impact of parameters on the performance of deep beams with different ratios of steel fibers, concrete compressive strength, type of steel fibers (short or long), and effective span-to-effective depth ratio. The effect of each parameter is examined relative to its effect on the fracture energy. Full article

To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing. Full article

Background: Large-volume paracentesis (LVP) of the peritoneal and pleural cavities is a common diagnostic and therapeutic intervention in patients with liver cirrhosis or advanced heart failure, which are both frequently associated with ascites or pleural effusion. Although generally regarded as a low-risk procedure, LVP may lead to complications such as intrapleural or intra-abdominal hemorrhage, and more commonly abdominal wall bleeding, as well as organ puncture and infection. Performing LVP in patients with coagulopathy or bleeding disorders, whether disease-related or due to anticoagulant therapy, poses a significant clinical challenge. The safety thresholds for such procedures remain inconsistent, and strategies to mitigate bleeding risk are still debated among professional societies. Methods: This review integrates institutional experience with a systematic synthesis of the current international literature to identify the safest and most effective approaches for performing LVP in patients with coagulopathy. The methodological framework included a comparative analysis of existing professional guidelines, as well as a critical evaluation of published evidence regarding risk stratification, pre-procedural correction strategies, and peri-procedural management. The evidence grading was assessed with the STAIR checklist. Results: Analysis of the evidence revealed substantial variability among professional recommendations concerning acceptable platelet and INR thresholds, as well as differing approaches to the management of patients receiving anticoagulant or antiplatelet therapy. Despite these discrepancies, the aggregated data support the conclusion that LVP can be performed safely in most patients with mild-to-moderate coagulopathy, provided that appropriate risk assessment and technical precautions are implemented. Conclusions: The resulting evidence-informed suggestions provide a practical framework for clinicians performing LVP in high-risk patients. By emphasizing systematic pre-procedural evaluation, individualized management of coagulopathy, and adherence to standardized procedural techniques, this work aims to promote safety, consistency, and confidence in the performance of large-volume paracentesis across diverse clinical settings. Full article

Microplastics (MPs) and nanoplastics (NPs), formed as a result of plastic product degradation, pose a global environmental threat by penetrating biological systems and inducing systemic pathological changes. This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, aims to analyze the molecular and cellular mechanisms of the toxic effects of MPs and NPs on the human cardiovascular, nervous, reproductive, urinary, and digestive systems. The primary mechanisms include oxidative stress, inflammation, mitochondrial dysfunction, apoptosis, autophagy, ferroptosis, and impaired barrier functions. In the cardiovascular system, MPs and NPs contribute to endothelial dysfunction, disorders of lipid metabolism, and fibrosis; in the nervous system, they promote neuroinflammation, pathological protein aggregation, and psychiatric disorders; in the reproductive system, they lead to hormonal imbalance and reduced fertility; in the kidneys, they cause inflammation, and fibrosis and lead to deterioration of kidney function; and in the gastrointestinal tract, they contribute to dysbiosis and metabolic disorders. The literature search was conducted in the PubMed, Web of Science, and Scopus databases without limitations on date, language, or access. Studies were selected based on criteria of transparency, statistical validity, sample representativeness, and correctness of data interpretation. The review emphasizes the necessity of an interdisciplinary approach to developing prevention and treatment strategies, including reduction in exposure, antioxidant and immunomodulatory therapy, and restoration of barrier functions and microbiota. The data obtained reveal research gaps and identify directions for further study. Full article

Depth from focus (DFF) is an optical, passive method that perceives the dense depth map of a real-world scene by exploiting the focus cue through a focal stack, a sequence of images captured at different focal distances. In DFF methods, first, a focus volume is computed, which represents per-pixel focus quality across the focal stack, obtained either through a conventional focus metric or a deep encoder. Depth is then recovered by different strategies: Traditional approaches typically apply an argmax operation over the focus volume (i.e., selecting the image index with maximum focus), whereas deep learning-based methods often employ soft-argmax for direct feature aggregation. However, applying a simple argmax operation to extract depth from the focus volume often introduces artifacts and results in an inaccurate depth map. In this work, we propose a deep framework that integrates depth estimates from both traditional and deep learning approaches to produce an enhanced depth map. First, a deep depth module (DDM) extracts an initial depth map from deep multi-scale focus volumes. This estimate is subsequently refined through a depth unfolding module (DUM), which iteratively learns residual corrections to update the predicted depth. The DUM also incorporates structural cues from traditional methods, leveraging their strong spatial priors to further improve depth quality. Extensive experiments were conducted on both synthetic and real-world datasets. The results show that the proposed framework achieves improved performance in terms of root mean square error (RMS) and mean absolute error (MAE) compared to state-of-the-art deep learning and traditional methods. In addition, the visual quality of the reconstructed depth maps is noticeably better than that of other approaches. Full article

The sustainable use of recycled aggregate concrete (RAC) is a critical pathway toward resource-efficient and environmentally responsible construction. However, the mechanical performance of RAC—particularly its elastic modulus—exhibits pronounced variability due to the heterogeneous quality and microstructural defects of recycled aggregates. This variability complicates the establishment of reliable predictive models and equations for elastic modulus estimation and restricts RAC’s broader structural implementation. Conventional empirical and machine-learning-based models (e.g., support vector machine, random forest, and artificial neural networks) are typically dataset-specific, prone to overfitting, and incapable of quantifying bias and uncertainty, making them unsuitable for heterogeneous materials data. This study introduces a bias-aware and more accurate predictive framework that integrates the Tabular Prior-data Fitted Network (TabPFN) with Monte Carlo Bias Estimation (MCBE)—for the first time applied in RAC materials research. A database containing 1161 RAC samples from diverse literature sources was established. This database includes key parameters such as apparent density ranging from 2270 kg/m³ to 3150 kg/m³, water absorption from 0.75% to 7.81%, replacement ratio from 0% to 100%, and compressive strength values ranging from 10.00 MPa to 108.51 MPa. MCBE quantified representational bias and guided targeted data augmentation, while TabPFN—pretrained on millions of Bayesian inference tasks—achieved R² = 0.912 and RMSE = 1.65 GPa without any hyperparameter tuning. Feature attribution analysis confirmed compressive strength as the most influential factor governing the elastic modulus, consistent with established composite mechanics principles. The proposed TabPFN–MCBE framework provides a reliable, bias-corrected, and transferable approach for modeling recycled aggregate concrete (RAC). It enables accurate predictions that are both trustworthy and interpretable, advancing the use of data-driven methods in sustainable materials design. Full article