Search Results (2,879)

Background/Objectives: Therapeutic options for patients with relapsed and refractory multiple myeloma (RRMM) have advanced substantially in recent years. In particular, T-cell-engaging therapies, including chimeric antigen receptor (CAR) T-cell therapy and bispecific antibodies (bsAbs), have emerged as highly effective treatment modalities. However, data on predictive biomarkers for response to these therapies remain limited. Patients currently receiving T-cell-engaging therapies are typically heavily pretreated and frequently exhibit clonal hematopoiesis. Clonal hematopoiesis, especially involving PPM1D mutations, may adversely affect the efficacy of T-cell-engaging therapies. Methods: We conducted a retrospective, single-center study including 27 patients with RRMM who were treated with bsAbs (teclistamab, elranatamab, or talquetamab) between June 2022 and September 2025 and for whom genetic material was available before bsAB treatment. We evaluated the impact of PPM1D mutations on treatment response, progression-free survival (PFS), and overall survival (OS). Results: The prevalence of PPM1D mutations in our cohort was 27%. Compared with patients without PPM1D mutations, mutation carriers showed a trend toward less deep remissions and demonstrated significantly inferior 6-month PFS (43% vs. 85%, p = 0.0272) and 6-month OS (57% vs. 90%, p = 0.0473). Conclusions: These findings suggest that PPM1D mutations may represent a promising biomarker in patients with RRMM treated with bsAbs. Larger, prospective studies are warranted to validate and further elucidate these observations. Full article

Chimeric antigen receptor (CAR) T-cell therapy is a new standard of care for patients with diffuse large B-cell lymphoma (DLBCL); however, studies including healthcare resource utilization (HRU) during routine care are lacking. Accordingly, a population-based study was conducted using linked administrative databases from Ontario, Canada. Patients with DLBCL that failed ≥2 lines of systemic therapy were included. Cox proportional hazard models estimated associations between covariates and overall survival (OS). Logistic, binomial and Poisson regression explored associations between covariates with toxicity and HRU. We identified 308 patients enrolled to receive CAR T-cell therapy of which 255 patients received CAR T-cells (mean age 59 years; 39% female). From the date of CAR T-cell infusion, the median OS was 25.0 months (95% CI, 21.6–28.1 months). Cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome data were available for 155 patients and were reported in 135 (87.1%) and 42 (27.1%) patients, respectively. Of those that received CAR-T cells, 172 patients (67%) were hospitalized with a median length of stay of 5 days (IQR, 0–20) and 243 (95%) had an emergency department visit without hospitalization. Our prospective population-based study demonstrates comparable efficacy and safety of CAR T-cell therapy in the real-world to the pivotal trials and highlights this as an efficacious and relatively safe treatment option for patients with DLBCL in routine clinical care. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

Background/Objectives: Chimeric antigen receptor T-cell (CAR-T) therapy has transformed outcomes in relapsed or refractory hematologic malignancies, but long-term cognitive outcomes remain poorly understood. We compared the incidence and time course of cognitive impairment and associated neurological complications after CAR-T therapy compared with autologous stem cell transplantation (ASCT). Methods: This retrospective, propensity-matched cohort study utilized the TriNetX US Collaborative Network (January 2014–April 2025). To ensure concurrent comparisons, ASCT recipients were restricted to an index date beginning in August 2017 or later. CAR-T recipients were matched 1:1 to ASCT recipients for demographics, disease, comorbidities, prior and concomitant treatments, and laboratory parameters. The primary endpoint was time to cognitive impairment, as defined by ICD-10 codes. Results: After comparing 3067 CAR-T patients (median follow-up 634 days) with 3067 ASCT patients (median follow-up 713 days), CAR-T recipients had a higher risk of cognitive impairment (HR 1.58; 95% CI 1.39–1.80; p < 0.001). Because the risks were not proportional (Schaenfeld p < 0.001), the difference was also expressed as restricted median survival time (RMST): CAR-T recipients spent approximately 25 and 53 days fewer days without cognitive impairment at 1 and 2 years, respectively (both p < 0.001). The risk was greatest at 30 days (HR 4.22; 95% CI 3.23–5.53), but remained elevated in control analyses at 30 and 90 days that excluded the acute ICANS window (HR 1.30 and 1.25, respectively; both p < 0.05). Neurological dysfunction, particularly encephalopathy (HR 2.04; 95% CI 1.73–2.40), was more common after CAR-T. Conversely, CAR-T was associated with a reduced risk of secondary acute myeloid leukemia (HR 0.46; 95% CI 0.38–0.55; p < 0.001). Conclusions: CAR-T therapy is associated with a higher risk of cognitive impairment that persists beyond the acute phase. As these are observational, code-based data, they should be interpreted as associations rather than evidence of a specific mechanism, and they highlight the need for informed consent discussions, long-term neurocognitive monitoring, and the development of neuroprotective strategies. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R² = 0.82; RMSE = 2.07 mg g⁻¹). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

Background/Objectives: Trace elements may influence autism spectrum disorder (ASD) severity through interactions with the gut microbiota and microbial metabolic functions, but calcium-related evidence remains limited. This cross-sectional study examined associations among hair calcium, gut microbial taxa, metabolic pathways, and behavioral phenotypes in children with ASD. Methods: We analyzed 183 children with ASD who had behavioral assessments, hair calcium measurements, and fecal shotgun metagenomic sequencing data. Participants in the lowest and highest calcium quartiles were first compared to characterize group-level microbiome differences. Full-sample analyses then tested associations among continuous hair calcium, microbial taxa, metabolic pathways, and behavioral measures after covariate adjustment. Benjamini–Hochberg false discovery rate correction was applied for multiple testing. Results: Hair calcium was positively associated with CARS, ATEC-Total, ATEC-1, and ATEC-3 scores, with the strongest associations involving ATEC-1 and ATEC-3. Alpha and beta diversity did not differ significantly between calcium quartile groups, but group-based microbiome analyses identified 63 differential species and 22 differential MetaCyc pathways. Full-sample integrated analyses connected calcium-associated microbial taxa, metabolic pathways, and ASD behavioral measures. Conclusions: Hair calcium was associated with ASD behavioral severity, selected gut microbial species, and microbial metabolic pathways. These findings support an association framework connecting longer-term calcium-related mineral profiles, gut microbial functional potential, and behavioral phenotypes, providing a basis for future longitudinal and multi-omics studies. Full article

Background: Pathological complete response (pCR) to neoadjuvant chemotherapy (NACT) in breast cancer is influenced by multiple tumor- and host-related factors, and readily available pretreatment biomarkers of response are still limited. This study aimed to evaluate the association between pretreatment systemic inflammatory and nutritional parameters and pCR assessed by the Miller–Payne grading system, with a specific focus on the independent predictive value of pretreatment serum albumin compared with established inflammatory ratios. Methods: A total of 226 patients with breast carcinoma who received NACT between May 2017 and September 2023 were retrospectively evaluated. Pretreatment laboratory parameters—including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), serum albumin, and the CRP/albumin ratio (CAR)—were recorded. Pathological response was assessed using the Miller–Payne grading system by two breast pathologists blinded to laboratory data. Univariable and multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were performed, complemented by bootstrap validation of the optimal cut-off, a sensitivity analysis using the contemporary ypT0/is ypN0 definition of pCR, and a subgroup analysis by molecular subtype. Results: pCR was observed in 41 patients (18.1%). Pretreatment serum albumin levels were significantly lower in responders than in non-responders (p = 0.027), whereas NLR, PLR, CRP, and CAR were not significantly associated with response. In multivariable analysis, pretreatment serum albumin, Ki-67, and HER2 status emerged as independent predictors of pCR. ROC analysis demonstrated moderate discriminatory ability for albumin (AUC = 0.64); the optimal cut-off was 4.22 g/dL (bootstrap 95% CI 3.50–4.53 g/dL), with values below this threshold associated with a higher likelihood of pCR. The association between low pretreatment albumin and pCR was particularly pronounced in the triple-negative subgroup (3.30 vs. 4.02 g/dL, p = 0.027). The albumin signal remained significant under the stricter ypT0/is ypN0 definition of pCR in univariable analysis (OR 0.47, p = 0.045). Conclusions: Pretreatment serum albumin, independent of systemic inflammatory ratios, is associated with pCR to NACT in breast cancer and may serve as a candidate biomarker for pretreatment risk stratification, particularly when interpreted alongside established tumor-related predictors such as Ki-67 and HER2 status. The association appears especially relevant in the triple-negative subgroup, suggesting that patients with TNBC and low pretreatment serum albumin may warrant heightened multidisciplinary attention during NACT. Validation in larger, prospective, multicenter cohorts is needed before routine clinical implementation. Full article

Urban mobility assessment requires evidence on congestion, travel time, speed loss, temporal variability, and peak imbalance. However, many city comparisons still rely on a single congestion ranking, which can obscure realised travel-time burden and road-network context. This study proposes a multidimensional diagnostic framework for seven Malaysian cities by integrating TomTom floating-car-data benchmarks, OpenStreetMap road-network indicators, and contextual population–vehicle background proxies. The framework applies data checking, unit harmonisation, direction adjustment, z-score standardisation, sensitivity analysis, and Ward hierarchical clustering. The results identify three exploratory profiles: high-congestion and long-travel-time cities, a city with high state-level vehicle-background context and strong peak asymmetry, and mixed lower-overall-pressure cities. Given the small sample, these profiles are interpreted as exploratory diagnostic patterns rather than stable city classifications. The framework supports sustainable mobility screening in data-scarce urban contexts. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) exhibits high post-resection relapse and early systemic dissemination rates. The level of circulating tumor cells (CTCs) correlates with early metastatic failure, motivating CTC interception strategies. Methods: In this hypothesis-driven review, we synthesized the contemporary evidence on PDAC staging and therapy, CTC detection (including portal versus peripheral sampling), and circulating tumor DNA (ctDNA)-based minimal residual disease (MRD), and evaluated the translational rationale for CTC-targeted adoptive immunotherapy focusing on CEACAM6 and CAR-T cells. Results: Prospective studies report higher portal versus peripheral CTC yields and stronger associations with relapse; tumor-informed ctDNA positivity in peri-operative and surveillance windows predicts shorter disease-free survival. CEACAM6 is overexpressed in PDAC and linked to invasion and metastasis, supporting antigen selection. However, target overexpression alone does not establish clinical suitability for adoptive cell transfer. Consequently, its therapeutic implementation must contend with assay heterogeneity, on-target/off-tumor risks, and the lack of interventional outcome data in PDAC, all of which remain key hurdles. Conclusions: CTC-targeting is biologically plausible and operationally measurable in PDAC. Consequently, a CEACAM6-directed CAR-T approach is proposed as a potential strategy for the interception of minimal residual disease (MRD). Randomized and biomarker-selected trials with composite MRD-clearance endpoints (CTC < LOQ and ctDNA-negative) may be justified to validate this interventional hypothesis. Full article

Background: Bronchiectasis is a heterogeneous chronic inflammatory airway disease characterized by recurrent exacerbations. Data on composite inflammatory biomarkers for assessing disease activity remain limited. Methods: This retrospective study included 97 patients with non-cystic fibrosis bronchiectasis categorized as stable (n = 39) or with exacerbated bronchiectasis (n = 58). Demographic, clinical, and laboratory data were analyzed, and inflammatory indices—NLR (neutrophil–lymphocyte ratio), PLR (platelet–lymphocyte ratio), SII (Systemic Immune-Inflammation Index), PIV (Pan-Immune-Inflammation Value), CAR (C-reactive protein-to-albumin ratio), and HALP score (hemoglobin × albumin × lymphocyte/platelet)—were calculated, followed by multivariate logistic regression and ROC analyses. Results: Patients with bronchiectasis exacerbations had a higher NLR, PLR, PIV, SII, and CAR and lower HALP (all p < 0.001). The C-reactive protein-to-albumin ratio demonstrated the highest discriminative ability (AUC = 0.995), followed by SII and NLR, while lower HALP and SII were independent predictors of exacerbation. The C-reactive protein-to-albumin and sedimentation-to-albumin ratios were strongly correlated with hospitalization duration (both p < 0.001). Conclusions: Composite inflammatory indices are strongly associated with disease activity in bronchiectasis. CAR showed excellent discriminative performance, while HALP and SII independently predicted exacerbation. These simple, cost-effective biomarkers may support risk stratification and clinical monitoring in routine practice. Full article

Delta-3-carene (CAR), a monoterpene derived from plant essential oils, exhibits promising biological properties, including anti-inflammatory, antioxidative, anxiolytic, and antimicrobial activities. Therefore, this study aimed to investigate the anti-inflammatory effects of CAR by analyzing the activity of this terpene on leukocyte activation through the evaluation of cell migration in in vitro and in vivo models. Cell viability analysis demonstrated that CAR (3, 10, 30, and 90 μg/mL) exerted no cytotoxic effects and significantly reduced in vitro neutrophil chemotaxis toward N-formylmethionyl-leucyl-phenylalanine (fMLP). Furthermore, CAR decreased phagocytosis in zymosan-stimulated neutrophils in vitro. In Swiss mice, oral CAR treatment, at doses of 25, 50, and 100 mg/kg, reduced inflammatory and antinociceptive parameters in zymosan-induced peritonitis, carrageenan-induced paw edema and mechanical hyperalgesia, and nociception induced by acetic acid and formalin models. In the persistent inflammation model (for 21 days) induced by complete Freund’s adjuvant (CFA), daily CAR treatment (50 mg/kg) reduced paw edema and mechanical hyperalgesia in all evaluated times at 6, 11, 16, and 21 days after CFA-induced inflammation. In conclusion, our data demonstrated that CAR modifies acute and chronic inflammatory responses, highlighting its potential therapeutic application in managing inflammation and pain. Full article