Search Results (2,085)

Thrombosis is a common complication in multiple myeloma (MM) patients treated with immunomodulatory drugs (IMiDs), including thalidomide, lenalidomide, and pomalidomide. When combined with anti-CD38 monoclonal antibodies, these agents are highly effective but may increase thrombotic events (TE), potentially delaying therapy. This exploratory, hypothesis-generating analysis, conducted within the MMVision mono-institutional prospective study, included 53 MM patients who initiated IMiD plus anti-CD38 therapy between May 2021 and December 2022 (median follow-up: 18 months). Treatment regimens comprised lenalidomide (n = 36) or thalidomide (n = 15) with daratumumab, and pomalidomide (n = 2) with isatuximab. Most patients (n = 38) received frontline therapy, and all were given thromboprophylaxis according to guidelines, mainly aspirin (73%). Five patients (9.4%) developed VTE after a median of 48 days, managed with short-term low-molecular-weight heparin (LMWH). Exploratory analysis of 27 clinical/laboratory parameters suggested possible associations between VTE and low levels of beta-2 microglobulin, ferritin, intact/free lambda light chains, and monocyte-to-lymphocyte ratio. Notably, four of the five VTEs occurred in patients without lytic bone disease, typically associated with bone-driven inflammation in MM. Although all patients received aspirin prophylaxis from treatment initiation, it remains unclear whether thrombosis would also have occurred among those with higher inflammatory burden. These preliminary observations may indicate that in patients with relatively lower inflammation, aspirin prophylaxis could be less effective, potentially favoring VTE onset. In two VTE cases, cytokine profiling showed decreased M-CSF, SCLF-β, and MIP-1α, with increased G-CSF, raising the hypothesis of distinct immune-inflammatory pathways contributing to TEs. Given the limited number of patients and thrombotic events, and the cytokine data available for only two VTE cases, these associations should be regarded as exploratory and interpreted with caution. Overall, these exploratory findings warrant validation in larger, independent cohorts and may help generate hypotheses on how inflammatory signatures influence thrombotic risk and prophylaxis efficacy in MM patients receiving IMiD/anti-CD38-based regimens. Full article

Binary code similarity detection serves as a critical front-line defense mechanism in cybersecurity, playing an indispensable role in identifying known vulnerabilities, detecting emergent malware families, and preventing intellectual property theft via code plagiarism. However, existing methods based on Control Flow Graphs (CFGs) often suffer from two major limitations: the inadequate capture of deep semantic information within CFG nodes, and the neglect of structural relationships across different functions. To address these issues, we propose Breg, a novel framework that synergistically integrates pre-trained semantic features with cross-graph structural features. Breg employs a BERT model pre-trained on a large-scale binary corpus to capture nuanced semantic relationships, and introduces a Cross-Graph Neural Network (CGNN) to explicitly model topological correlations between two CFGs, thereby generating highly discriminative embeddings. Extensive experimental validation demonstrates that Breg achieves leading F1-scores of 0.8682 and 0.8970 on Dataset3. In real-world vulnerability search tasks on Dataset4, Breg achieves an MRR@10 of 0.9333 in the challenging MIPS32-to-x64 search task, a clear improvement over the 0.8533 scored by the strongest baseline. This underscores its superior effectiveness and robustness across diverse compilation environments and architectures. To the best of our knowledge, this is the first work to integrate a pre-trained language model with cross-graph structural learning for binary code similarity detection, offering enhanced effectiveness, generalization, and practical applicability in real-world security scenarios. Full article

Metal–organic frameworks (MOFs) are promising materials for environmental remediation, particularly in photocatalysis. In this work, a novel ZMIP nanocomposite was fabricated by integrating MIP-202(Zr) bio-MOF with ZnO nanoparticles. For the first time, ZnO nanoparticles were green-synthesized using water lettuce extract and incorporated into MIP-202(Zr) via a mild hydrothermal route. The resulting hybrid was applied as a visible-light photocatalyst for carbamazepine (CBZ) degradation in real pharmaceutical wastewater. Structural analyses (XRD, FTIR, TEM, EDS) verified the successful incorporation of ZnO into the MIP-202(Zr) framework. The composite exhibited a narrowed bandgap of 2.74 ± 0.1 eV compared to 4.05 ± 0.06 eV for pristine MIP-202 and 3.77 ± 0.04 eV for ZnO, highlighting enhanced visible-light utilization in ZMIP. Operational parameters were optimized using response surface methodology, where CBZ removal reached 99.37% with 84.39% TOC mineralization under the optimal conditions (90 min, pH 6, 15 mg/L CBZ, 1.25 g/L catalyst). The catalyst maintained stable performance over five reuse cycles. Radical quenching and UHPLC-MS analyses identified the dominant reactive oxygen species and generated intermediates, elucidating the degradation mechanism and pathways. Beyond CBZ, the ZMIP photocatalyst effectively degraded other pharmaceuticals, including doxorubicin, tetracycline, paracetamol, and ibuprofen, achieving degradation efficiencies of 82.93%, 76.84%, 72.08%, and 67.71%, respectively. Application on real pharmaceutical wastewater achieved 78.37% TOC removal under the optimum conditions. Furthermore, the supplementation of the photocatalytic system by inorganic oxidants ameliorated the degradation performance, following the order KIO₄ > K₂S₂O₈ > KHSO₅ > H₂O₂. Overall, ZMIP demonstrates excellent activity, reusability, and versatility, underscoring its potential as a sustainable photocatalyst for real wastewater treatment. Full article

Approximately 3.8% of the global population suffers from depressive disorders, posing a substantial public health challenge exacerbated by the COVID-19 pandemic due to widespread unemployment and prolonged social isolation. The difficulty in objectively quantifying psychological states underscores the need for effective stress assessment methods. Herein, we developed a portable electrochemical cortisol sensor (PECS) for accurate mental stress assessment. The PECS consists of a screen-printed carbon electrode decorated with gold nanoparticles and a molecularly imprinted polymer (MIP) synthesized via electropolymerization. The as-prepared PECS demonstrates a wide and linear detection range from 1 fM to 1 μM, an ultra-low detection limit of 0.4112 fM and a high sensitivity of 15.518 nA∙lg(nM⁻¹)∙cm⁻². This work provides new possibility of developing soft bioelectronics for non-invasive and real-time mental health monitoring. Full article

Pseudomonas aeruginosa causes ocular and other infections and quickly acquires antimicrobial resistance. Polymyxin B and colistin are last-line agents against resistant P. aeruginosa, yet even resistance to these is increasing. Antimicrobial peptides (AMPs) are also being developed as new antibiotics, but resistant mechanisms to polymyxins might also cause resistance to these AMPs. This study evaluated whether isolates with differing polymyxin resistances also showed elevated minimum inhibitory concentrations (MICs) to the human cathelicidin LL-37 and a synthetic AMP, Mel4. Forty isolates of P. aeruginosa, mostly collected in India and Australia, were assessed for minimum inhibitory concentrations (MICs) by broth microdilution in cation-adjusted Mueller–Hinton broth. Whole genome sequences were analyzed using NCBI BLAST (version 2.17.0). SNPs vs. MIC associations were evaluated with Fisher’s exact test. Sixty-five percent of isolates were resistant to polymyxin B, and 80% to colistin. Polymyxin B MICs ranged from 0.5 to 512 µg/mL, with 32.5% showing intermediate resistance and 22.5% being highly resistant (MIC ≥ 256 µg/mL). MICs for polymyxin B and colistin were strongly correlated with each other (Spearman’s R ≥ 0.6; n = 40; p ≤ 0.001). LL-37 showed moderate correlations with polymyxin B, colistin, and Mel4, whereas Mel4 showed weaker correlations with polymyxin B or colistin (R < 0.4). Genomic analysis identified SNPs in mipB (V469M, G441S) as being associated with the MICs to all the antimicrobials. Strains with MICs between 64 and 512 µg/mL were significantly more likely to harbor nalC (E153Q/D) or the mipB variants (p < 0.05). Higher polymyxin MICs were associated with elevated MICs to LL-37 and, to a lesser extent, Mel4, suggesting partial shared resistance among membrane active peptides. Defining the effect of the SNPs and clinical relevance of AMP cross-resistance may inform future therapies and safer contact lenses. Full article

Unmanned Aerial Vehicle-assisted Edge Computing (UAV-EC) leverages UAVs as aerial edge servers to provide computation resources to user equipment (UE) in dynamically changing environments. A critical challenge in UAV-EC lies in making real-time adaptive offloading decisions that determine whether and how UE should offload tasks to UAVs. This problem is typically formulated as Mixed-Integer Nonlinear Programming (MINLP). However, most existing offloading methods sacrifice strategy timeliness, leading to significant performance degradation in UAV-EC systems, especially under varying wireless channel quality and unpredictable UAV mobility. In this paper, we propose a novel framework that enhances offloading strategy timeliness in such dynamic settings. Specifically, we jointly optimize offloading decisions, transmit power of UEs, and computation resource allocation, to maximize system utility encompassing both latency reduction and energy conservation. To tackle this combinational optimization problem and obtain real-time strategy, we design a Quality of Experience (QoE)-aware Online Offloading (QO2) algorithm which could optimally adapt offloading decisions and resources allocations to time-varying wireless channel conditions. Instead of directly solving MIP via traditional methods, QO2 algorithm utilizes a deep neural network to learn binary offloading decisions from experience, greatly improving strategy timeliness. This learning-based operation inherently enhances the robustness of QO2 algorithm. To further strengthen robustness, we design a Priority-Based Proportional Sampling (PPS) strategy that leverages historical optimization patterns. Extensive simulation results demonstrate that QO2 outperforms state-of-the-art baselines in solution quality, consistently achieving near-optimal solutions. More importantly, it exhibits strong adaptability to dynamic network conditions. These characteristics make QO2 a promising solution for dynamic UAV-EC systems. Full article

The hybrid energy storage system (HESS) that combines battery with hydrogen storage exploits complementary power/energy characteristics, but most studies optimize capacity and operation separately, leading to suboptimal overall performance. To address this issue, this paper proposes a bi-level co-optimization framework that integrates deep reinforcement learning (DRL) and mixed integer programming (MIP). The outer layer employs the TD3 algorithm for capacity configuration, while the inner layer uses the Gurobi solver for optimal operation under constraints. On a standalone PV–wind–load-HESS system, the method attains near-optimal quality at dramatically lower runtime. Relative to GA + Gurobi and PSO + Gurobi, the cost is lower by 4.67% and 1.31%, while requiring only 0.52% and 0.58% of their runtime; compared with a direct Gurobi solve, the cost remains comparable while runtime decreases to 0.07%. Sensitivity analysis further validates the model’s robustness under various cost parameters and renewable energy penetration levels. These results indicate that the proposed DRL–MIP cooperation achieves near-optimal solutions with orders of magnitude speedups. This study provides a new DRL–MIP paradigm for efficiently solving strongly coupled bi-level optimization problems in energy systems. Full article

Perfluorooctanesulfonate (PFOS) is a typical persistent organic pollutant, which presents a significant risk to the ecosystem and human health. Therefore, the development of a highly sensitive and effective detection technique for PFOS has aroused wide concern. In this study, for the mesoporous metal–organic frameworks (MOFs), Cr-MIL-101 were used as the precursor. And the poly(3,4-ethylenedioxythiophene) (PEDOT) using as molecularly imprinted polymers (MIPs) was loaded on Cr-MIL-101 to form a core–shell structure. The obtained Cr-MIL-101@PEDOT/MIP composites integrate the high specific surface area of Cr-MIL-101 and the specific recognition capability of PEDOT/MIP. The glassy carbon electrode (GCE) interface modified by them can specifically adsorb PFOS through electrostatic interactions, coordination by Cr metal nodes, hydrophobic interaction, and hydrogen bonding, etc. The adsorbed PFOS molecules could block the active sites at the electrode interface, causing the current decay of the redox probe. Following the quantitative analysis of peak current decay values using the Langmuir model and the Freundlich–Langmuir model, a wide detection range (0.1–200 nM) and a low detection limit (0.025 nM) were obtained. Characterization techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), X-ray photoelectron spectroscopy (XPS), and electrochemical methods were employed to validate the fabrication of the composites. Moreover, Cr-MIL-101@PEDOT/MIP/GCE showed satisfactory stability, repeatability, and selectivity, providing an effective method for the detection of PFOS in practical samples, showing a wide prospective application. Full article

Polar patterns and topological defects are ubiquitous in active matter. In this paper, we study a paradigmatic polar active dumbbell system through numerical simulations, to clarify how polar patterns and defects emerge and shape evolution. We focus on the interplay between these patterns and morphology, domain growth, irreversibility, and compressibility, tuned by dumbbell rigidity and interaction strength. Our results show that, when separated through MIPS, dumbbells with softer interactions can slide one relative to each other and compress more easily, producing blurred hexatic patterns, polarization patterns extended across entire hexatically varied domains, and stronger compression effects. Analysis of isolated domains reveals the consistent presence of inward-pointing topological defects that drive cluster compression and generate non-trivial density profiles, whose magnitude and extension are ruled by the rigidity of the pairwise potential. Investigation of entropy production reveals instead that clusters hosting an aster/spiral defect are characterized by a flat/increasing entropy profile mirroring the underlying polarization structure, thus suggesting an alternative avenue to distinguish topological defects on thermodynamical grounds. Overall, our study highlights how interaction strength and defect–compression interplay affect cluster evolution in particle-based active models, and also provides connections with recent studies of continuum polar active field models. Full article

UHPC has been found to have excellent freeze–thaw durability in cold regions. Previous UHPC testing performed has mostly focused on concrete with compressive strength above 21 ksi (145 MPa). In this study, testing was conducted to determine at what strength level concrete transitions to provide excellent freeze–thaw (F–T) performance. Non-proprietary concrete samples were made for freeze–thaw durability from four different concrete mixture designs: 12–15 ksi, 15–18 ksi, 18–21 ksi, and 21+ ksi (83–145+ MPa), and these were tested according to ASTM C666, using 1.5% steel fibers. The samples were made for three different curing regimens: limewater curing in a fog room, simulated precast curing, and steam curing. Low-temperature differential scanning calorimetry (DSC) and mercury intrusion porosimetry (MIP) tests were carried out to reveal the freeze–thaw mechanism of the concrete samples. All mixtures with compressive strength above 15 ksi (103 MPa) performed excellent in freeze–thaw testing with no damage seen. Steam curing was found to negatively affect the freeze–thaw performance at the lowest strength level tested. Full article

Staphylococcus aureus (S. aureus) is one of the most common hospital-acquired pathogens and poses a serious threat to patients with weakened immune systems. Transmission can occur through foodborne illness, skin infections, abscess formation, and bloodstream invasion. The most severe complication arises when S. aureus infects the heart, leading to valve damage and potentially progressing to heart failure. In addition, many strains have developed strong resistance to conventional antibiotic therapies, making treatment increasingly difficult. These challenges highlight the importance of early detection for effective prevention and management. This research focuses on the development of a polymer composite incorporating hydroxyproline for the preparation of molecularly imprinted polymers (MIPs) designed for the rapid detection of S. aureus. The sensing platform, based on electrochemical principles, enabled sensitive and efficient analysis of bacterial samples. The sensor exhibited a broad analytical range, detecting S. aureus from 1 to 10,000 CFU/mL, with a detection limit as low as 1.031 CFU/mL. Selectivity testing against Pseudomonas aeruginosa, Candida albicans, and Escherichia coli confirmed high specificity toward S. aureus. These findings highlight the potential of this MIP-based electrochemical sensor as a reliable tool for rapid bacterial detection in clinical and environmental settings. Full article

In this work, we have synthesized a more acid-stable variant of the classic chromophore difluoro-Bodipy by substituting the difluoro ligands at boron with cyano groups. This dicyano-Bodipy variant allowed the in situ incorporation during the MOF formation under acidic conditions and was investigated for the first time as dye@MOF composites using both post-synthetic and in situ incorporation into the zirconium-based metal–organic frameworks (MOFs) UiO-66, MOF-808, DUT-67, and MIP-206. The successful incorporation of dicyano-Bodipy was confirmed by PXRD, N₂ sorption, digestion UV–Vis, and fluorescence spectroscopy. Depending on the incorporation method used, significant lower BET surface areas could be determined. The luminescence properties of the resulting dicyano-Bodipy@MOF composites from the in situ incorporation had up to almost eight-fold extended photoluminescent lifetimes of 9.0 ns, compared to the neat dye in its solid state with 1.2 ns, which suggests the formation of a solid solution in which the incorporated Bodipy is protected from external influences within a well-defined MOF pore. The quantum yield could be enhanced to as high as 77% through post-synthetic incorporation into the MOF DUT-67, compared to the neat dye in its solid state, with 9%. Full article

This study systematically investigates the effects of individual and combined incorporation of graphene oxide (GO) and nano-silica sol (NS) on the macroscopic properties and microstructure of cement-based grouting materials, with emphasis on their synergistic mechanisms. A series of macroscopic tests including setting time, fluidity, bleeding rate, and mechanical strength were conducted, complemented by multi-scale microstructural characterization techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), mercury intrusion porosimetry (MIP), and Fourier-transform infrared spectroscopy (FTIR). The results demonstrate that both NS and GO effectively reduce setting time and bleeding rate while enhancing mechanical strength; however, NS exhibits a more pronounced adverse effect on fluidity compared to GO. The hybrid system displays a distinct transition from synergy to antagonism: under low-dosage co-incorporation (2 wt% NS + 0.01 wt% GO), the flexural and compressive strengths increased by 13.5% and 45.5%, respectively, relative to the reference group. Microscopic analysis revealed that the synergistic interaction between the pozzolanic effect of NS and the templating effect of GO under this condition optimizes hydrate morphology and pore structure, leading to enhanced performance. Conversely, excessive dosage of either component induces agglomeration, resulting in microstructural deterioration and performance degradation. This study establishes optimal dosage ranges and combination principles for NS and GO in cement-based materials, providing a theoretical foundation for designing high-workability and high-strength cementitious composites. Full article

Maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP) are commonly used proxies to measure respiratory muscle strength. Current literature lacks recent large-scale normative reference values (NRV) for these pressures in healthy Singaporean adults. Moreover, no consensus exists on the variables that could influence MIP and MEP. This cross-sectional study aims to: (1) establish the NRV of MIP and MEP; (2) determine the correlations of variables that could influence these pressures; and (3) develop regression equations using non-spirometry variables to estimate reference values for MIP and MEP in the healthy Singaporean population aged 21 to 80 years. MIP and MEP were measured alongside demographic and anthropometric data collected from 391 participants (202 females, 189 males) recruited via convenience sampling. Median MIP and MEP values were significantly higher in males (112 and 85 cmH₂O) than in females (83 and 64 cmH₂O). Spearman correlations revealed significant associations between MIP/MEP and gender, height, weight, body mass index (BMI), waist–hip ratio, and spirometric variables. Regression models using age, gender, height, and weight explained 27.5% and 32.1% of the variance in MIP and MEP, respectively. This study updated the NRV of MIP and MEP and provided practical predictive equations for assessing respiratory muscle strength in Singapore. Full article

Albendazole (ABZ) is a broad-spectrum anthelmintic drug whose residual presence in food and the environment raises public health concerns, requiring rapid and sensitive methods of detection. In this work, a sensitive Fabry–Pérot interferometer (FPI) probe was fabricated by realizing a cavity located at the tip of a single-mode optical fiber core with a molecularly imprinted polymer (MIP) for ABZ detection. The fabrication process involved the development of a photoresist-based micro-hole filled by the specific MIP via thermal polymerization. Interferometric measurements obtained using the proposed sensor system have demonstrated a limit of detection (LOD) of 27 nM, a dynamic concentration range spanning from 27 nM (LOD) to 250 nM, and a linear response at the nanomolar level (27 nM–100 nM). The selectivity test demonstrated no signal when interfering molecules were present, and the application of the sensor for ABZ quantification in a commercial pharmaceutical sample provided good recovery, in accordance with bioanalytical validation standard methods. These results demonstrate the capability of a MIP layer-based FPI probe to provide low-cost and selective optical-sensing strategies, proposing a competitive approach to traditional analytical techniques for ABZ monitoring. Full article