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A systematic stepwise optimization framework for rapid multi-analyte UPLC–MS/MS plasma analysis with integrated sustainability assessment
Abstract A stepwise optimization guidance model for rapid multi-analyte UPLC–MS/MS is proposed to report recurring limitations in bioanalytical method development. The model involves six stages: (1) physicochemical profiling, (2) extraction strategy selection, (3) chromatographic optimization, (4) mobile phase selection, (5) method validation, and (6) greenness evaluation. The applicability of this framework was demonstrated by determining Rifaximin, Ciprofloxacin, and Fluconazole in spiked human plasma, using Ibuprofen as an internal standard. Physicochemical profiling guided the selection of ionization mode and prediction of solubility behavior. Based on the model recommendations, liquid–liquid extraction using dichloromethane was selected to achieve effective analyte recovery. Chromatographic optimization and mobile phase selection through the proposed framework supported the use of rapid isocratic separation on a C18 column using a methanol/water mixture (95:5, v/v) as mobile phase. The method validation, performed as an integral stage of the workflow according to regulatory guidelines, demonstrated exceptional linearity (r² ≥ 0.999). The final stage of the model involved evaluating environmental applicability using AGSA and EPPI analyses. The proposed sequential framework is intended to serve as a methodological reference for analysts employing LC–MS/MS platforms, to establish a structured and reproducible analytical workflow that advances ecological sustainability in multi-analyte LC–MS/MS bioanalytical applications.
Professional quality of life among migrant and non-migrant mental health staff in Germany: a gender-sensitive cross-sectional study
Abstract Germany increasingly relies on an internationally recruited migrant workforce, like Vietnamese migrants, to address shortages in the mental healthcare sector. This cross-sectional study examined differences in the Professional Quality of Life (ProQOL), measured through the subscales burnout (BO), secondary traumatic stress (STS) and compassion satisfaction (CS), among Vietnamese migrant and non-migrant mental health professionals ( N = 312) in Germany. Participants completed an online survey in German or Vietnamese including the ProQOL – 5 Scale, as well as sociodemographic and work-related variables. Multiple linear regression models identified salary across all subscales and psychiatric pre-conditions for BO and STS as significant predictors for ProQOL outcomes. Analyses of covariance compared adjusted group means across migration and gender. Vietnamese migrant professionals report significantly lower CS and higher STS compared to non-migrant professionals, while no significant migration-related differences were observed for BO. Considering gender, women exhibit significantly higher CS than men, whereas no significant gender differences emerged for BO or STS. No significant migration × gender interaction effects were found. Findings indicate migration background is associated with relevant differences in ProQOL among mental health staff in Germany, underscoring the need for targeted support, like an inclusive workplace culture and work-related social network. Further research should incorporate direct measures of migration-related stressors to better understand underlying mechanisms and longitudinal designs.
Cross-organ transfer learning for tumor microenvironment classification from colorectal to gastric cancer histopathology
Abstract Transfer learning offers a promising strategy for extending computational pathology models from data-rich to annotation-scarce cancer types, yet the transferability of tumor microenvironment (TME) representations across gastrointestinal organs remains underexplored. Here, we trained Swin Transformer, ConvNeXtV2, and UNI2-h deep learning models on ~ 100,000 annotated colorectal cancer histopathology patches (NCT-CRC dataset) and evaluated their generalization to the HMU-GC gastric cancer dataset (31,096 patches) under zero-shot and few-shot conditions. In the zero-shot setting, evaluation was performed on the full HMU-GC dataset, whereas each few-shot experiment used independently generated training, validation, and test splits. Zero-shot transfer yielded limited performance (macro-F1: 49.15–53.63%), revealing a substantial domain gap between colorectal and gastric histology. Few-shot adaptation with only 5% labeled gastric patches improved macro-F1 to 63.25–68.53%. The best configuration—Swin Transformer with Reinhard stain normalization and 20% labeled target data—achieved 72.26% macro-F1 and 72.22% accuracy, corresponding to an absolute accuracy gain of 18.49% points over the same model’s zero-shot baseline. These findings provide preliminary, single-pair evidence that cross-organ transfer of TME representations between colorectal and gastric histology is feasible but constrained by domain differences. Establishing generalizable cross-organ transfer will require validation across multiple source and target cohorts.
Water access, sanitation, and hygiene (WASH): exploring the interplay of practices and diarrhea in children under five in North Kordofan State, Sudan
Machine learning-based prognostic model for trauma and surgical ICU patients: the role of peak glucose and glycemic variability
Pi-Loc: a Pareto indoor localization using deep learning in 5GB
Abstract Ultra-accurate indoor localization is essential for critical applications such as emergency drone tracking, healthcare operations, and autonomous systems that rely on 5G New Radio (NR) and beyond networks. Sub-centimeter positioning accuracy is increasingly necessary for safe and effective operation in complex indoor environments. However, existing localization methods, including global positioning systems (GPS), face fundamental limitations when dealing with complex blockage conditions, signal heterogeneity, and the coexistence of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions, particularly in single-cell positioning scenarios. In response to these challenges, this work introduces Pi-Loc, a novel Pareto indoor localization framework that explicitly addresses the trade-off between LoS and NLoS signal conditions through multi-objective optimization. Pi-Loc operates in two phases: first, Pareto optimization is applied to min-max normalized 5G NR channel state information (CSI) features, minimizing the competing LoS and NLoS localization errors simultaneously; second, the Pi-Loc convolutional network is trained on the Pareto-selected optimal CSI feature weightings. Extensive experiments on multiple simulated 5G NR benchmark datasets including three DeepMIMO indoor ray-tracing scenarios compliant with the 3GPP 5G NR cluster delay line (CDL) channel model, a 3GPP Indoor Office scenario, and an NYUSIM complex blockage dataset demonstrate that Pi-Loc outperforms established deep learning baselines (NN, DNN, LSTM, BiLSTM) in both accuracy and convergence efficiency, achieving near-mm-level positioning accuracy on simulated DeepMIMO datasets. Ablation studies confirm the framework’s stability and the individual contribution of each CSI feature category.
Dual-source mesoporous nanosilicas from horsetail plant and chemical precursors show structural and textural evidence for vitamin C loading and release
A qualitative study of stakeholder perspectives on the challenges of the rapid expansion of medical student enrollment in Iran
Hypertension and associated factors among HIV/AIDS patients on highly active anti-retroviral therapy (HAART)
Genomic evidence that Enterobacter strain MG-EB028 and Enterobacter nematophilus strain 93 form a lineage distinct from Enterobacter nematophilus sensu stricto
Tempol ameliorates STZ-induced T1DM potentially via enhancing AMPK signaling, suppressing pro-apoptotic activity, promoting progenitor cells recruitment, and restoring glucose transporters level
Integration of a clinical survival model into the KM-plotter to identify high-risk populations in breast cancer
Abstract We aimed to establish clinically meaningful survival models in breast adenocarcinoma to support pharmacological research and guide therapy development. Using data from over 1.1 million patients in the SEER database, we performed univariate and multivariate Cox survival analyses on infiltrating ductal carcinoma cases stratified by pathological and demographic variables. By quantifying hazard ratios and Kaplan–Meier survival estimates across clinical subgroups, we identified patient cohorts with significantly reduced breast-cancer-specific survival. We further approximated the life-years lost in each subgroup to assess the cumulative survival burden and to prioritize cohorts for therapeutic intervention. Significant survival differences were observed across molecular subtypes and pathological categories, with ER-negative, triple-negative, node-positive, high-grade, advanced-stage, and larger tumors showing markedly reduced 60-month survival. For instance, survival declined from 96% in Stage I to 33% in Stage IV disease, from 98% in Grade 1 to 80% in Grade 4 tumors, and from 95% in tumors 10–19.9 mm to 51% in those ≥ 100 mm. Receptor-negative, high-grade tumors, despite their lower prevalence, account for a disproportionately large fraction of total life-years lost. To facilitate reproducibility and exploratory analyses, we integrated these clinical survival models into an upgraded version of the Kaplan–Meier plotter, which now supports user-defined cohort analysis based on clinicopathological variables. In summary, here we provide a large-scale, clinically grounded survival framework to identify and optimize target cohorts for pharmacology developments in breast cancer.
Dual-band dual-beam millimeter-wave antenna using dual circular microstrip rings
Abstract The growing demand for high-capacity millimeter-wave (mmWave) communication systems requires compact, high-gain antennas that provide efficient spatial coverage while remaining simple to fabricate and integrate. This article presents a novel dual-band, dual-beam microstrip antenna based on a dual circular-ring radiator. Unlike existing designs, the proposed antenna achieves wide dual-band, dual-beam operation without vias, parasitic elements, or metamaterial loading, resulting in a compact, low-profile architecture that is straightforward to fabricate using standard printed circuit board (PCB) technology. Excitation of higher-order resonant modes, combined with optimization of the ring geometry and ground plane, produces symmetric dual-beam radiation in the quasi-E plane across two wide operating bands of 24.2–30 GHz and 32.5–43.5 GHz. Measured results agree closely with simulations, validating the antenna’s wide impedance bandwidth and stable radiation characteristics. The antenna generates dual beams at ± 40 $$^{\circ }$$ in the lower band and ± 24 $$^{\circ }$$ in the upper band, achieves a peak realized gain of 9 dBi at 38 GHz, and maintains gains exceeding 6 dBi across both bands. These characteristics make the proposed antenna a practical and cost-effective solution for next-generation indoor mmWave communication systems.
Recovery and utilization of fluorescent carbon dots from slag-washing wastewater of waste incineration power plants for cell imaging
Nonsingular fast terminal sliding mode control based on super-twisting finite-time ESO for aerospace electro-hydraulic load simulators under large loads
Clinical relevance of MRI-informed motor progression phenotypes in early Parkinson’s disease
Behavior of reinforced concrete two-way slabs reinforced with carbon FRP bars under flexural loads
Abstract This study presents an experimental, numerical, and analytical investigation of the flexural behavior of two-way high-strength concrete (HSC) slabs reinforced with carbon fiber-reinforced polymer (CFRP) bars. Seven slabs with dimensions of 1500 mm × 1500 mm were tested, including one steel-reinforced reference slab and six CFRP-reinforced slabs with two thicknesses, 100 and 120 mm, and three CFRP reinforcement ratios. For the 100 mm slabs, increasing the CFRP reinforcement ratio increased the ultimate load by 13.8% and 24.3% relative to S100-CFRP1, while the corresponding increases for the 120 mm slabs were 31.1% and 48.0% relative to S120-CFRP1. Increasing the slab thickness from 100 to 120 mm enhanced the ultimate load by 27.7%, 47.2%, and 52.1% for the corresponding CFRP-reinforced specimens. A mechanically normalized comparison between the steel-reinforced reference slab and the CFRP-reinforced slab with the same nominal reinforcement ratio showed that the two specimens were not mechanically equivalent. The CFRP-reinforced slab had lower effective reinforcement stiffness but a higher strength-based mechanical reinforcement index, resulting in a higher ultimate load but lower deformation capacity. Crack observations confirmed flexural failure with more localized cracking in the CFRP-reinforced slabs. A nonlinear finite element model was developed in ABAQUS and validated against the experimental results, with an average ultimate-load prediction error of approximately 7.2%. Analytical predictions based on ECP 208–2019 and ACI 440.1R-15 showed average predicted-to-experimental ultimate-load ratios of 1.43 and 1.27, respectively, indicating that ACI 440.1R-15 provided closer estimates than ECP 208–2019 for the tested CFRP-reinforced slabs.
Network-level and motor consequences of longitudinal perivascular diffusivity alterations indexed by DTI-ALPS in Parkinson’s disease
Leveraging ultrashort echo time magnetic resonance imaging to quantify sex-specific adaptations to the glenoid labrum across the lifespan
Abstract The glenoid labrum is a collagen‑rich fibrocartilage structure critical to shoulder stability yet susceptible to age‑related degeneration. Quantitative, in‑vivo assessment of its composition remains limited. Ultrashort echo time MRI with magnetization transfer (UTE‑MT) enables estimation of macromolecular fraction (MMF), a biomarker sensitive to the collagen‑rich matrix content of the glenoid labrum. This study investigated the effect of age, sex, menopause status, and arm dominance on the glenoid labrum MMF. Forty‑two healthy adults (25 females, mean age: 47 ± 15 years; 17 males, mean age: 38 ± 15 years) underwent bilateral shoulder MRI on a 3T system using a double echo steady state sequence (DESS) for segmentation and a custom UTE‑MT protocol to quantify MMF. Labra were segmented from DESS images, and voxel‑wise MMF was computed. Mean MMF was calculated for each arm. A linear mixed-effects model with a random intercept for subject to account for within-subject dependence tested the effects of age, sex, arm dominance, and their respective interactions. A linear mixed effect model with a random intercept for subject tested the effect of menopause status among females on mean MMF. Males exhibited higher mean labral MMF than females (male: 40.4 ± 7.3%, female: 39.6 ± 7.2%; p = 0.016). Age showed no main effect on mean MMF. However, a significant sex‑by‑age interaction indicated a steeper age‑related decline in males ( p = 0.0113). There was no significant effect of arm dominance on mean MMF (dominant: 41.1 ± 7.6%, non-dominant: 38.8 ± 6.9%; p = 0.412), and there was no significance found for the interactions of dominance and sex, dominance and age, or dominance, sex, and age. Among females, mean MMF did not differ by menopause status (premenopausal 39.4 ± 7.4% vs. postmenopausal 39.7 ± 7.1%; p = 0.761). UTE‑MT MRI detects sex‑specific differences in glenoid labrum MMF, with a significant sex‑by‑age interaction despite no main effect of age. These findings support MMF’s potential as a non‑invasive quantitative marker of labral composition and motivates longitudinal studies to define clinically meaningful thresholds and regional variation.