Search Results (171)

Background: Although multiple pomalidomide-based combinations are active in relapsed and/or refractory multiple myeloma (RRMM), comparative data to guide regimen selection remain limited. Methods: A total of 230 patients with RRMM from 12 centers in China who received pomalidomide-based regimens were included in this retrospective analysis. Overall response rate (ORR) and progression-free survival (PFS) were compared across regimens incorporating bortezomib or ixazomib (V/IPD), carfilzomib (KPD), or daratumumab (DPD), and multivariable analyses were performed to identify prognostic factors. Results: The overall ORR was 73.9%, with rates of 63%, 79%, and 85% in the V/IPD (n = 66), KPD (n = 69), and DPD (n = 95) cohorts, respectively. ORR differed significantly between V/IPD and DPD (p = 0.0165), driven by a higher proportion of ≥VGPR in the DPD group. The median PFS for the entire cohort was 17.4 months (95% CI: 13.7–20.1), compared with 15.4 months (95% CI: 12.8–20.5), 14.2 months (95% CI: 6.9–not estimable), and 19.2 months (95% CI: 15.1–24.9) for V/IPD, KPD, and DPD, respectively, without significant differences. In multivariable analysis, DPD was associated with improved ORR (HR 4.83, p < 0.001) but not with PFS. R-ISS stage III predicted inferior response (HR 0.35, p = 0.04), whereas ≥3 prior lines of therapy correlated with shorter PFS (HR 1.77, p = 0.012). Adverse events were predominantly hematologic, with limited grade 3–4 toxicity and no treatment-related mortality. Conclusions: This multicenter real-world analysis clarifies the relative positioning of commonly used pomalidomide-based regimens in RRMM and underscores the importance of treatment timing and disease stage in optimizing outcomes. Full article

Three-edge bearing (TEB) tests and a crack-width-dependent load-carrying model were used to assess the combined effects of recycled glass fiber-reinforced polymer (rGFRP) short fibers and glass fiber-reinforced polymer (GFRP) bars in concrete pipes. Using the force method, a circumferential statically indeterminate ring analysis was formulated to obtain internal forces at critical sections and the neutral-axis position. Fiber distribution was simulated by means of Monte Carlo sampling, and single-filament pull-out tests were fitted to relate embedded length to pull-out force, enabling calculation of the fiber-bridging contribution at cracked sections. Ten specimen types with different bar/fiber schemes were tested under external pressure to validate the model. Predicted cracking and ultimate loads agreed with measurements, with most errors within ±20%. Adding 1% (vol.) rGFRP fibers increased the cracking load by 11.81% and the ultimate load by 0.45%. Without fibers, replacing steel bars with equal-area GFRP bars increased the cracking load by 1.35% but reduced the ultimate load by 35.45%. For all specimens, the load–maximum crack-width relation was strongly linear (R² > 0.93). The proposed approach and dataset support engineering use of recycled GFRP materials for crack control and load-carrying design of concrete pipes. Full article

In practice, the emerging shared electric bicycles battery-swapping systems face weather disturbances and time-window lateness, which can reduce travel efficiency and degrade operational reliability. To facilitate the operation reliability and management robustness, this study builds a scenario-based location–routing optimization model that links station siting with replenishment routing under two weather scenarios, no rain and rain. The first stage selects sites and determines battery-swapping station construction decisions before scenario realization. The second stage reacts through scenario-dependent depot assignment and routing and scheduling decisions. The objective functions are to minimize average cost while restraining tail risk through an explicit worst-case term, yielding an adjustable efficiency–resilience balance. The modeling constraints impose a minimum service level, preserve route feasibility under scenario travel times, and prevent structural shortcuts. An improved genetic algorithm is proposed to solve the model. The algorithm adopts construction encoding and scenario-wise assignment encoding, applies feasibility repair before evaluation, and constructs executable routes during decoding with local improvement. Experiments demonstrate that the proposed method achieves better objective values than benchmark methods and exhibits stable convergence. Case study shows that rain increases transportation and lateness-related costs. The System Resilience Analysis shows that the robust penalty term reduces variable operating loss under rain by 5.33% and cuts the cost shock from no rain to rain by 32.82%, demonstrating improved resilience under adverse weather. Full article

To address the challenges of reduced delivery efficiency, complex routing decisions, and limited system robustness in cooperative transportation involving a single drone and an autonomous vehicle under dynamic weather conditions, this study investigates the optimization of drone–autonomous vehicle collaborative delivery in complex and uncertain environments. The objective is to improve task execution efficiency while enhancing the adaptability of the transportation system to dynamic disturbances. To this end, an optimization model is developed by incorporating weather variations, drone–vehicle coordination constraints, and the spatiotemporal characteristics of delivery tasks. Based on this model, a dedicated solution algorithm is proposed to achieve efficient joint optimization of route planning and task allocation in complex environments. Numerical results demonstrate that, for the same randomly generated instance, the drone–truck collaborative delivery strategy reduces the delivery time from 414.55 to 385.10 compared with the truck-only scheme, corresponding to an improvement of 7.1%, thereby confirming the effectiveness of the collaborative transportation strategy. Furthermore, when weather factors are taken into account and drone–truck cooperation is allowed, the proposed algorithm reduces the delivery time from 392.84, obtained by a conventional algorithm, to 338.39, yielding a performance improvement of 13.8%. These results verify the effectiveness and superiority of the proposed algorithm in dynamic weather environments. Overall, the proposed method significantly improves the efficiency of the cooperative transportation system and provides theoretical support and methodological guidance for drone–autonomous vehicle collaborative delivery in complex environments. Full article

This study is based on isotope (δ¹⁸O, δ²H, ¹⁴C), hydrochemical, and groundwater-level data from the past 47 years in the central North China Plain (NCP). It uses methods such as mathematical statistics, Piper diagrams, Gibbs models, and ion ratios to investigate the characteristics of changes in the groundwater flow field, hydrochemistry, and isotopes across various aquifers in the Hengshui funnel area before and after the implementation of groundwater exploitation reduction measures (GWER). Furthermore, it reveals the driving mechanisms of these measures’ effects on hydrochemical characteristics and isotopic variations. The results show the following: (1) The hydrochemical type of shallow groundwater (SG) before GWER was primarily Cl▪SO₄-Na▪Ca, which diversified to Cl▪SO₄-Na and SO₄▪Cl-Na types after GWER; the deep groundwater (DG) type changed from Cl▪SO₄-Na to Cl-Na. (2) Before GWER, the hydrochemical composition of SG and DG was primarily controlled by the dissolution of silicates, salt rocks, and evaporites. After GWER, the hydrochemical composition of DG primarily originated from the dissolution of evaporites and salt rocks, accompanied by enhanced cation exchange. (3) The δ¹⁸O of SG was affected by the recharge of irrigation return water, changing from enrichment to depletion before and after the GWER. The δ¹⁸O value in DG changed from depletion to enrichment, and the groundwater age changed from older to younger after the GWER. The GWER altered the hydrodynamics, weakened the hydraulic connectivity, and led to changes in the evolution of the hydrochemistry. The findings have direct implications for water quality and promoting the sustainable utilization of deep groundwater in the NCP’s central funnel area. Full article

This study reports the first successful isolation and characterization of exosome-like nanoparticles from walnut kernels (WELNs). The isolated WELNs exhibited a typical cup-shaped morphology with an average diameter of 139.7 ± 67.5 nm, a concentration of 7.4 × 10¹¹ particles/mL, and a zeta potential of −17.47 ± 4.06 mV. Proteomic and small RNA sequencing analyses confirmed the presence of diverse proteins and microRNAs within WELNs. In vitro assays demonstrated their potent antioxidant capacity, with radical scavenging rates of 67.54% against ABTS⁺ and 48.59% against DPPH⁺ at 102 μg/mL and IC₅₀ values of 89.7 μg/mL and >102 μg/mL for scavenging of ABTS⁺ and DPPH⁺ radicals, respectively. Cytotoxicity assays indicated no adverse effects on RAW264.7 macrophage viability at concentrations up to 60 μg/mL. In LPS-stimulated RAW264.7 macrophages, WELN treatment (20–60 μg/mL) dose-dependently mitigated oxidative stress by reducing intracellular ROS levels (down to 81.22% of the control at 60 μg/mL) and malondialdehyde (MDA) content while restoring the activities of antioxidant enzymes catalase (CAT) and superoxide dismutase (SOD). Furthermore, WELNs significantly suppressed the production of nitric oxide (NO) and pro-inflammatory cytokines TNF-α, IL-6, and IL-1β (reduced to approximately 30.8%, 22.7%, and 23.6% of LPS-induced levels, respectively, at 60 μg/mL). Mechanistic investigation revealed that the anti-inflammatory effect was mediated through the inhibition of the MAPK signaling pathway, as evidenced by decreased phosphorylation of p38, ERK, and JNK. In conclusion, WELNs exhibit dual anti-inflammatory and antioxidant properties. This study provides the first evidence of bioactivity for walnut-derived exosome-like nanoparticles, advancing the mechanistic understanding of walnuts’ health benefits and highlighting their potential as a natural component for functional food applications. Full article

Fluoride (F⁻) contamination in deep groundwater threatens drinking water security, yet its enrichment is commonly governed by coupled nonlinear hydrogeochemical feedbacks that are difficult to resolve with linear diagnostics alone. Here, we integrate an explainable deep learning framework (HydroAttentionNet + SHAP) with thermodynamic and mass-conservative inverse modeling (PHREEQC) to quantitatively link data-driven thresholds to mineral water processes in a multi-aquifer system. Using 258 deep-well samples, we delineate a robust evolution pathway from background to ultra-high-fluoride (Ultra-High F⁻, ≥1.5 mg/L) waters. HydroAttentionNet achieves strong predictive skill (R² = 0.77) and reveals a clear mechanistic tipping behavior: alkalinity (HCO₃⁻/CO₃²⁻) is the primary trigger for F⁻ activation, while progressive Na+ enrichment and Ca²⁺ depletion act as amplifiers by suppressing a(Ca²⁺) and weakening fluorite precipitation capacity. PHREEQC simulations confirm a coupled “salinization–decalcification–fluoridation” loop in which (i) evaporite dissolution elevates ionic strength (salt effect) and supplies Na⁺ to promote Na–Ca exchange, and (ii) carbonate re-equilibration drives calcite precipitation as an efficient Ca sink, offsetting ~45.8% of Ca²⁺ inputs; together, these processes maintain fluorite undersaturation and sustain net fluorite dissolution, contributing 56.6% of newly added dissolved F⁻ in evolved end-members. Monte Carlo health risk assessment (10,000 iterations) indicates substantial intergenerational inequity: 67.9% of children exceed the non-carcinogenic risk threshold (HQ > 1), compared with 29.3% of adults. Sensitivity analysis identifies source-water fluoride concentration as the dominant driver (Spearman r = 0.93), implying that supply-side interventions (defluoridation, well-screen optimization, and blending with low-F sources) are substantially more effective than behavioral measures. Full article

Long-range hopping plays a crucial regulatory role in non-Hermitian topological systems. This paper systematically studies a non-Hermitian Su–Schrieffer–Heeger (SSH) model that incorporates both long-range hopping and spin–orbit coupling (SOC) within the framework of the generalized Brillouin zone (GBZ). We reveal that long-range hopping can not only actively suppress the non-Hermitian skin effect, but can also cooperate with SOC to jointly modulate the stability regions of topological phases. SOC controls topological transitions through real or imaginary coupling properties and enhances the robustness of edge states. By constructing the GBZ and establishing the non-Bloch bulk–boundary correspondence, we demonstrate that the topological zero modes are entirely determined by the non-Bloch winding number. This study clarifies the key role of long-range hopping as a core regulatory parameter and provides a new paradigm for achieving the synergistic control of topological states and localized properties in non-Hermitian systems through designed couplings. Full article

Nanofertilizers have attracted increasing attention as an approach to improve the low nutrient use efficiency of conventional fertilizers, in which only a limited fraction of applied nitrogen, phosphorus, and potassium is ultimately taken up by crops. Beyond their capacity to minimize nutrient losses, nanofertilizers have attracted increasing attention for their possible role in addressing environmental issues, including soil eutrophication and the contamination of groundwater systems. Owing to their nanoscale characteristics, including large specific surface area and enhanced adsorption capacity, these materials enable more precise nutrient delivery to the rhizosphere and sustained release over extended periods, while also influencing soil–plant–microbe interactions. In this review, nanofertilizers are classified into six major categories—macronutrient-based, micronutrient-based, organic, controlled-release, composite, and nano-enhanced formulations—and representative examples and preparation routes are summarized, including green synthesis approaches and conventional chemical methods. The agronomic mechanisms associated with nanofertilizer application are discussed, with emphasis on enhanced nutrient uptake, modification of soil physicochemical properties, and shifts in microbial community composition. Reported studies indicate that nanofertilizers can increase crop yield across different crop species and formulations, while also contributing to improved nutrient cycling. Despite these advantages, several limitations continue to restrict their broader adoption. These include uncertainties regarding long-term environmental behavior, relatively high production costs compared with conventional fertilizers, and the absence of well-defined regulatory and safety assessment frameworks in many regions. Overall, this review highlights both the opportunities and challenges associated with nanofertilizer application and points to the need for further development of cost-effective formulations and standardized evaluation systems that account for their distinct environmental interactions. Full article

Background: Drug-induced liver injury (DILI), a major type of adverse drug reaction, has become one of the leading causes of acute liver injury and liver failure worldwide. Its clinical significance lies not only in acute hepatocyte necrosis and functional failure but also in its role as a key initiating factor for liver cancer progression. Therefore, early diagnosis of DILI is of great importance. Methods: This study employed ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to perform widely targeted metabolomics analysis on acetaminophen (APAP)-induced liver injury mice and healthy mice. Results: UPLC-QTRAP-MS/MS identified 41 differentially expressed metabolites primarily involved in glycerophospholipid metabolism, arginine and proline metabolism, primary bile acid biosynthesis, and glutathione metabolism pathways. The significant elevation of serum and hepatic alanine aminotransferase (ALT) and aspartate aminotransferase (AST) confirmed the successful establishment of the drug-induced liver injury (DILI) model. ROC curve analysis indicated 11 metabolites with AUC values exceeding 0.90 as potential biomarkers, including (R)-2-Hydroxybutyric acid, Glu-Gln, γ-Glu-Gln, 2-Methyllactic acid, L-Serine, Hyodeoxycholic acid, 3-Epideoxycholic acid, and Glycochenodeoxycholic acid 7-sulfate. Conclusions: We propose that these differential metabolites may serve as candidate biomarkers for DILI. Our findings provide a novel metabolomic signature derived directly from the injured tissue and offer a theoretical foundation for further research into early diagnosis of drug-induced liver injury. Full article

Parental educational anxiety poses a significant risk to children’s academic development. This two-stage study first establishes Parental Mathematics Education Anxiety (PMEA) as a unique construct and then examines the complex effects of a positive suggestion intervention. Study 1, a questionnaire-based investigation, revealed that PMEA is a significant and independent negative predictor of children’s mathematics achievement, distinct from parents’ general state anxiety or their own mathematics anxiety. It also identified socioeconomic factors, such as family income and parental education, as key drivers of PMEA. Study 2 employed an experimental design to test an intervention, revealing that the effectiveness of positive suggestions is not universal but is significantly moderated by the three-way interaction of PMEA level, child type (with/without math learning difficulties), and suggestion frequency. Notably, for non-math-difficult children, frequent positive suggestions from high-anxiety parents were found to be potentially detrimental (a “backfire effect”), whereas for math-difficult children in high-anxiety homes, a higher frequency of suggestion was necessary to yield benefits. These findings deepen the understanding of PMEA’s mechanisms and underscore the necessity of moving beyond one-size-fits-all approaches toward differentiated, context-aware intervention strategies in family education. Full article

Co-incorporating rice straw and Chinese milk vetch (CMV) residues can enhance soil organic carbon (SOC) sequestration and productivity. However, limited information exists regarding its effects on SOC and nitrogen (N) pools as well as the sustainability of rice production in the middle and lower reaches of the Yangtze River Basin. A 3-year field experiment was conducted to assess the effects of co-incorporating rice and CMV residues into paddy soils with chemical-N reduction on SOC and total N (TN) sequestration, SOC and N fractions, grain yields and the sustainable yield index (SYI) in Ma’anshan City, Anhui Province. The treatments included winter fallow–rice rotation without or with both rice straw incorporation and fertilization, as the control (CK and WF-IF, respectively), and rice-CMV rotation with the co-incorporation of rice and CMV residues under 100%, 80%, and 70% recommended N fertilization (CMV-IF, CMV-MIF and CMV-LIF, respectively). Compared with the CK, the CMV-IF significantly increased the rice grain yield and the SYI by 82.1% and 90.4%, respectively. The SOC and TN stocks under CMV-IF were significantly enhanced by 6.3% and 26.4%, respectively, relative to the CK. The CMV-IF exhibited the highest soil active organic C (AOC) and active total N (ATN) contents, followed by CMV-MIF, CMV-LIF, WF-IF, and CK. Microbial biomass C and microbial biomass N were the primary components of soil AOC and ATN, respectively, and linked more explicitly to the SYI than other soil C and N parameters. Therefore, the co-incorporation of rice and CMV residues, coupled with 70~80% recommended N fertilization, might represent an environmentally friendly field management practice for rice production in the middle and lower reaches of the Yangtze River Basin. Full article

Potato (Solanum tuberosum) is one of the world’s most important food crops, with tuber sink strength and starch deposition determining yield, quality, and processing performance. While starch is the dominant carbohydrate reserve, its accumulation is tightly linked with protein metabolism. Patatin, the major soluble storage protein, constitutes up to 40% of total tuber protein. In addition to serving as a nitrogen and carbon reserve, patatin exhibits lipid acyl hydrolase (phospholipase A₂-like) activity, suggesting roles in membrane remodeling and stress signaling. This dual identity places patatin at the intersection of storage, metabolic regulation, and defense. A structured review of studies published between 1980 and 2025 was developed using PubMed, Web of Science, Frontiers, and MDPI databases. Prioritized research included molecular, physiological, and multi-omics analyses of patatin expression, regulation, and function under optimal and stress conditions. Evidence indicates that patatin contributes to carbon–nitrogen balance and sink strength by affecting sucrose import, vacuolar osmotic capacity, and starch biosynthesis. Under drought, salinity, and pathogen stress, patatin transcript levels, protein stability, and enzymatic activity shift, leading to reduced starch deposition, altered sugar accumulation, osmoprotection, and reallocation toward defense responses. Despite these insights, major knowledge gaps remain. These include isoform-specific roles, integration into sugar–hormone regulatory networks, and field-scale responses under fluctuating environments. Future progress will require integrated multi-omics, fluxomics, and proximity-labeling approaches, combined with CRISPR-based isoform editing and promoter engineering. Targeting patatin as both a biomarker and an engineering node offers opportunities to develop climate-ready potato cultivars with improved starch yield, tuber quality, and stress resilience. Full article

Spike length is a critical trait influencing the yield potential of wheat (Triticum aestivum L.). However, there has been limited research on spike-length-related genes in wheat. Moreover, the scarcity of stable markers for spike-related traits has restricted marker-assisted selection-based breeding. In this study, a novel long-spike mutant material (LS1) was generated from wheat variety ‘Aikang 58’ (AK58) using ethyl methanesulfonate. We established an F₂ segregating population by crossing AK58 with LS1. Morphological analyses of this population indicated that spike length is a dominant quantitative trait regulated by multiple genes. Bulked segregant analysis (BSA) technology was used to preliminarily identify nine candidate regions associated with spike length traits. These regions were mainly in a 7.22 Mb interval (673.84–713.26 Mb) on chromosome 5A and in a 2.34 Mb interval (714.83–717.69 Mb) on chromosome 7B. Twelve candidate genes were identified within these regions. Furthermore, two kompetitive allele specific polymerase chain reaction (KASP) markers (KASP-LS1-681460621 and KASP-LS1-692013966) associated with spike length traits were developed. Both KASP markers effectively genotyped parental lines and the F₂ population. Our study results provide a theoretical foundation for the genetic improvement of spike-length-related traits in wheat. Full article

Nanotechnology plays a crucial role in promoting precision agriculture and environmental management. This review integrates the latest advances in nanotechnology in the fields of pollution detection, agrochemicals, and stress resistance, and quantifies the significant enhancements brought by nanomaterials (NMs). NMs used in biosensors enable highly sensitive, low detection limit, and highly accurate detection of environmental pollution, plant growth status, and soil conditions, while achieving precise drug delivery and reducing environmental pollution. Furthermore, NMs can be combined with agrochemicals or directly act on plants to promote growth, reduce pests and diseases, and enhance stress resistance by altering plant physiological processes and microbial functions. This review focuses on the application value of nanotechnology in detection, smart chemicals, and stress resistance, and analyzes current challenges and risks in technology, biosafety, regulatory challenges, and scalability. Finally, it points out future directions for utilizing nanotechnology to advance smart agriculture, precision agriculture, and green bio-industrialization. Full article