Search Results (4,096)

Background: Kinesins are ATP-dependent molecular motors that mediate intracellular transport, cytoskeleton remodeling, and abiotic stress responses in plants. The KIN gene family remains poorly characterized in Prunus mume. This study aimed to explore the evolutionary features and cold-responsive functions of the PmKIN gene family. Methods: A systematic genome‑wide analysis was performed in P. mume. We performed phylogenetic classification, conserved domain detection, gene duplication and selection pressure analysis, cis-element prediction, tissue expression profiling, cold stress expression assay, and protein interaction network prediction. Results: Fifty PmKIN family members were defined and grouped into 10 subfamilies. K14 subfamily contained the largest number of members. All members harbor conserved motor domains, and segmental duplication drove the expansion of this gene family, which was overall constrained by purifying selection. The PmKIN homologous genes were highly conserved among Rosaceae species, including Prunus persica, Prunus armeniaca, Prunus avium, and Malus domestica. Our promoter analysis detected abundant cis-elements for light, hormone, cold, and drought signals in PmKIN genes, especially in the K14 and K7 subfamilies. These genes displayed tissue-biased expression, with roots and stems showing much higher transcript levels than fruits. Under low-temperature stress, the tolerant cultivar appeared to mount a relatively rapid PmKIN upregulation, whereas the sensitive one seemed to show a sluggish response and poor recovery. Three members (PmKIN22/27/35) were suggested to be potentially critical in low-temperature-response regulation. A 285-pair interaction network predicted PmKIN45 as the central hub, and functional predictions imply that PmKIN proteins may be linked to microtubules, hormone pathways, and stress signaling, possibly coordinating growth and low-temperature responses. Conclusions: This work screens candidate genes associated with low-temperature responses in P. mume, providing genetic resources for the molecular breeding of cold-resistant cultivars and expanded cultivation of P. mume and other ornamental horticultural species. Full article

The long-pressure and short-exhaust ventilation method can effectively reduce dust concentration in mine roadways (tunnels) and alleviate hazards to workers. However, it is usually difficult to resolve the problem of high dust concentrations at the driver operation area. Accordingly, this paper proposes an air curtain dust isolation device suitable for the driver operation area, and computational fluid dynamics (CFD) methods were also employed to carry out numerical simulations and parameter optimization of its dust isolation efficiency. The research findings indicate that the device creates a transparent air curtain in front of the driver, effectively preventing dust from spreading towards the driver operation area. However, its dust isolation performance was significantly affected by the jet velocity. When the jet velocity was within the range of 0–10 m/s, the dust concentration at the driver operation area increased as the velocity rose; when the jet velocity increased to 15 m/s, the dust concentration fell sharply, with the average concentrations of total dust and PM_2.5 reaching the lowest values of 9.52 mg/m³ and 3.87 mg/m³, respectively. However, when the jet velocity was further increased to 20 m/s, the dust concentrations rose significantly again. Compared with conditions without an air curtain, at the optimum jet velocity of 15 m/s, the dust isolation efficiency reached 58.1%, indicating that this device can effectively safeguard the driver’s occupational health. The findings of this study may serve as a reference for dust protection at the driver operation area in similar working environments, such as tunnel excavation and metal mining. Full article

Objective: Traffic-related air pollution is recognised as a significant environmental risk factor associated with obesity and metabolic dysfunction. Although the positive effects of regular exercise on metabolic health are well established, its protective role against traffic-related air-pollution-induced metabolic dysfunction has not been fully elucidated. This study was conducted to investigate the effects of diesel exhaust exposure and moderate exercise on the development of obesity, metabolic changes, adipokine profile, and inflammatory response in rats. Materials and Methods: In this randomised controlled experimental study, 48 female Wistar Albino rats were randomised into six experimental groups. The animals were exposed to diesel exhaust simulating traffic-related air pollution (average 300 μg PM2.5/m³) for 2 or 4 h daily over an eight-week period. Rats in the exercise groups underwent moderate-intensity treadmill exercise for 30 min at a speed of 15 m/min, five days a week, over the same period. At the end of the study, obesity indicators, serum lipid profile, glucose metabolism parameters, adipokines and inflammation markers were assessed. Results: Diesel exhaust exposure caused a significant increase in final body weight, BMI, Lee index and VAI values (p < 0.001). Furthermore, levels of LDL cholesterol, total cholesterol, triglycerides, glucose, adiponectin, leptin, CRP, IL-37, IL-1β, IL-6 and TNF-α increased significantly, whilst HDL cholesterol and insulin levels decreased significantly (p < 0.001). The most pronounced metabolic and inflammatory changes were observed in the 4 h exhaust gas exposure group. Elevations in pro-inflammatory cytokines, accompanied by increased visceral adiposity, supported the development of systemic metabolic inflammation. Exercise significantly reduced weight gain, visceral adiposity, dyslipidaemia, hyperglycaemia, adipokine imbalance and the inflammatory response (p < 0.001). However, exercise could not fully reverse all the changes associated with long-term diesel exhaust exposure. Conclusions: Traffic-related diesel exhaust exposure led to metabolic disorders in rats, characterised by visceral adiposity, dyslipidaemia, impaired glucose homeostasis, adipokine imbalance and chronic low-grade inflammation. Regular moderate-intensity exercise significantly reduced these adverse effects but could not eliminate them entirely. The findings suggest that traffic-related air pollution may contribute to the development of obesity via inflammation-mediated metabolic mechanisms, and that regular physical activity may serve as an important protective strategy in limiting these effects. Full article

Background: Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Few approaches combine population-level surveillance with individual-level monitoring within a single analytical framework. Methods: This work developed a dual machine learning framework. Study 1 modeled the daily count of respiratory admissions (chapter X of the ICD-10) in São Paulo, Brazil, from 2017 to 2022 as a nowcasting task, combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated by walk-forward over 30 bimonthly windows. Study 2 applied an XGBoost and Random Forest pipeline to 913 diaphragmatic-breathing sessions from 17 patients aged 9 to 16 years in the Respire Bem system, with Asthma Control Test and salivary cortisol represented using evidence-based synthetic simulation. Results: Study 1 achieved a mean MAE of 18.22, RMSE of 23.99, and R² of 0.675, exceeding the seasonal baseline by 41.5%, with a significant advantage over all three baselines (Wilcoxon and Diebold–Mariano, p ≤ 0.038). Ablation showed each single-component configuration to be significantly worse than the full hybrid, but removing the environmental block cost only 0.27 admissions per day, an effect indistinguishable from zero. SHAP rankings were stable across windows (Kendall W = 0.640), led by NO₂, PM2.5, and temperature. In Study 2 the pipeline ran end-to-end on real behavioral data, but because the outcomes were simulated, no predictive-accuracy metric is reported. Conclusions: Study 1 delivers a validated population-level nowcasting model whose accuracy rests mainly on the temporal structure of the series. Study 2 contributes a real behavioral dataset and a reproducible pipeline; the clinical validity of the digital biomarkers remains open and requires prospective work with directly measured outcomes. Full article

Background: Autoantibodies are central to the diagnosis and risk stratification of systemic sclerosis (SSc), but their prevalence and clinical associations may vary across populations. This study aimed to evaluate the prevalence and immuno-clinical associations of different autoantibodies in the Rheumatic Diseases Portuguese Register systemic sclerosis cohort. Methods: This was a multicentre observational registry-based study that used data from the Reuma.pt SSc module. Patients were divided according to autoantibody status, and associations between autoantibody expression and clinical data were assessed using appropriate statistical tests, with Bonferroni correction applied for multiple comparisons. Multivariable binary logistic regression was performed for clinically relevant outcomes, with adjustment for sex, age at diagnosis and disease duration. Results: A total of 1080 patients were included, 87.5% female, with a mean age at last evaluation of 60.2 ± 14.6 years and a mean disease duration of 12.4 ± 10.0 years. Limited cutaneous SSc was the most frequent clinical category (57.4%), followed by diffuse cutaneous SSc (17.7%), very early diagnosis of systemic sclerosis (VEDOSS; 12.3%), overlap syndromes (9.8%) and SSc sine scleroderma (2.8%). Antinuclear antibodies were present in 93.4% of patients. Anti-centromere antibodies (ACAs) were the most frequent SSc-specific autoantibodies (54.6%), followed by topoisomerase I antibodies (ATAs; 21.8%). ACA positivity was associated with limited cutaneous disease; older age at diagnosis; and lower frequency of interstitial lung disease (ILD), myositis and flexion contractures. ATA positivity was associated with diffuse cutaneous disease, male sex, higher modified Rodnan skin score (mRSS), digital ulcers, flexion contractures, oesophageal involvement and ILD. Among less frequent autoantibodies, anti-Pm/Scl antibodies were associated with myositis, joint involvement, calcinosis and ILD; anti-U1RNP antibodies with younger age, MCTD overlap, myositis and joint involvement; anti-RNA polymerase III antibodies (ARAs) with scleroderma renal crisis and higher mRSS; anti-U3RNP antibodies with diffuse cutaneous disease and renal involvement; and anti-Ku antibodies with overlap syndromes. Conclusions: In this large real-world SSc cohort, autoantibody status was strongly associated with distinct clinical phenotypes, confirming its value for disease stratification. While most established immuno-clinical associations were reproduced, some differences from international cohorts were observed, supporting the need for population-specific validation of autoantibody associations in SSc. Full article

Objectives: To characterize the clinical and genetic features of Phelan–McDermid syndrome (PMS) in a Chinese prenatal and postnatal cohort, explore genotype–phenotype correlations, and provide evidence to support prenatal genetic counseling. Methods: G-banded karyotyping, single-nucleotide polymorphism arrays (SNP arrays), copy number variation sequencing, and trio whole-exome sequencing were used for genetic diagnosis. Fifteen patients with PMS (11 prenatal and 4 postnatal) were retrospectively enrolled. Genomic visualization, protein structural prediction, and phenotypic heatmap analyses were conducted to analyze genotype–phenotype associations. Results: The prenatal and postnatal detection rates of PMS were 0.061% and 0.49%, respectively. Only 13.3% of cases were detected by karyotyping, whereas 14 cases were confirmed by SNP array analysis. All 22q13 deletions (69.4 kb–8.5 Mb) involved SHANK3, and all copy number variants were de novo. A novel SHANK3 frameshift variant, c.3513_3514delCC, was identified and predicted to result in protein truncation leading to intellectual disability. Prenatal cases mainly presented non-specific ultrasound anomalies (63.6%, 7/11), with fetal growth restriction, renal malformations, and cardiovascular defects being the most frequent findings. A notable 36.4% (4/11) of prenatal cases were complicated by missed abortion, whereas postnatal patients showed predominant neurodevelopmental impairments. In our cohort, larger deletions were observed in cases presenting with more extensive multisystem involvement, a pattern that mirrors findings from recent large-cohort studies. central nervous system abnormalities were predominantly linked to SHANK3 haploinsufficiency, while multi-system malformations seemed to correlate with the cumulative loss of multiple genes across the 22q13 region, in keeping with current hypotheses on contiguous gene effects. Conclusions: PMS presents obvious prenatal–postnatal phenotypic heterogeneity. SHANK3 is the core pathogenic gene, and deletion may influence the extent of multisystem involvement. Combined genetic testing can improve the accuracy of prenatal diagnosis and counseling for PMS. Full article

Aquatic antibiotic pollution represented by ofloxacin (OFX) causes serious ecological hazards and endangers public health due to the high persistence and bioaccumulation of antibiotic residues. Conventional water treatment techniques are insufficient for OFX elimination, limited by low removal efficiency, high energy consumption, and poor operational stability. Herein, a novel MoS₂/CuS heterojunction composite was fabricated via a hydrothermal method and applied to an ultrasound-driven piezocatalysis-coupled peroxymonosulfate (PMS) advanced oxidation system for OFX wastewater remediation. The introduction of CuS effectively remedies the inherent shortcomings of pristine MoS₂, including insufficient active sites and rapid photogenerated carrier recombination. The constructed heterojunction induces a strong interfacial built-in electric field, which significantly accelerates the migration of piezoelectric charges. The synergistic photo-piezoelectric effect further promotes continuous PMS activation and facilitates the massive generation of reactive oxygen species (ROS). The influences of key operating parameters and common water inorganic anions on OFX degradation performance were systematically investigated. Radical trapping experiments confirmed the synergistic mechanism between piezocatalysis and PMS activation during the catalytic reaction. The optimized MoS₂/CuS heterojunction exhibits remarkable OFX degradation efficiency and excellent cyclic stability. This work provides a feasible strategy for the rational design and fabrication of high-efficiency piezocatalysts and offers a promising technical route for the remediation of refractory antibiotic wastewater via piezocatalysis-coupled PMS advanced oxidation. Full article

Background/Objectives: Cytochrome P450(CYP)2C19 activity varies widely among individuals. As genetic factors, CYP2C19*2 and CYP2C19*3 alleles reduce CYP2C19 activity, while the CYP2C19*17 allele increases CYP2C19 activity. However, environmental and physiological factors can also influence individual CYP2C19 activity. In this study, we searched for novel endogenous biomarkers for CYP2C19 activity using CYP2C19 gene polymorphism data combined with results of untargeted metabolomic analysis. Methods: 431 general adults analyzed in the Kyoto J-MICC Study and 255 patients who visited Oita University Hospital were studied. Plasma samples were pretreated by solid-phase and liquid-liquid extraction and subjected to untargeted metabolomic analysis using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Based on CYP2C19 gene polymorphism data, participants were classified into extensive metabolizers (EM), intermediate metabolizers (IM), and poor metabolizers (PM). Compounds showing significant differences in abundance among the three groups were considered candidate compounds for predicting CYP2C19 activity. The predictive performance of candidate compounds for CYP2C19 PM status was evaluated using covariate-adjusted receiver operating characteristic (ROC) analysis. Results: The normalized abundance of compounds with m/z 160.1342, 303.2319 (a fatty acyl or prenol lipid), 314.2309, 449.3238, 653.3021, 792.5744, and 811.5988 (a glycerophospholipid or sphingolipid), and 902.5404 (a fatty acyl) differed significantly among CYP2C19 EM, IM, and PM groups (p < 0.05), and these eight compounds were considered candidate compounds. Covariate-adjusted ROC analysis showed that none of the candidate compounds significantly improved the discrimination of CYP2C19 PM status. Conclusions: Untargeted metabolomics combined with CYP2C19 gene polymorphism data yielded eight compounds associated with CYP2C19 phenotype. Further studies are needed to evaluate the usefulness of these compounds as biomarkers of CYP2C19 activity. Full article

Objectives: This study aimed to investigate the relationships among mismatch repair (MMR) protein status, clinicopathological characteristics, and Programmed Death-Ligand 1 (PD-L1) expression in endometrial cancer, focusing specifically on heterogeneity within MMR-deficient (MMRd) tumors defined by distinct protein loss patterns. Methods: This retrospective study enrolled 675 patients with surgically confirmed endometrial cancer at a tertiary medical group between January 2017 and June 2025. Patients were stratified according to specific MMR protein loss patterns. Clinicopathological parameters, the International Federation of Gynecology and Obstetrics (FIGO) stage, and PD-L1 combined positive score were compared across groups. Results: The cohort comprised 502 MMR-proficient (MMRp) and 173 MMR-deficient (MMRd) patients. Based on their specific MMR protein loss patterns, the MMRd cases were further stratified into three subgroups: MutLαcomplex loss (MLH1 ± PMS2, n = 105), MutSαcomplex loss (MSH2 ± MSH6, n = 60), and combined MutSα/MutLαcomplex loss (n = 8). Compared with MMRp tumors, MMRd tumors exhibited significantly lower BMI, higher tumor grade, more frequent lymphovascular space invasion, and elevated PD-L1 expression. Moreover, the 2023 FIGO staging system demonstrated superior performance in capturing the locally aggressive features of MMRd tumors relative to the 2009 edition. Within the MMRd cohort, the overall difference in combined positive score reached nominal significance (p = 0.049) but did not survive FDR correction (q = 0.290). Nevertheless, at the 1% and 5% cutoffs, MutLα-deficient tumors consistently showed higher PD-L1 positivity than MutSα-deficient tumors, with both associations remaining significant after FDR adjustment (q = 0.046 for both). Further subgroup analysis within the MutLα loss pathway revealed that isolated MLH1 loss was associated with a lower rate of CPS ≥ 1% (85.7%) compared with isolated PMS2 loss (91.3%) and combined MLH1/PMS2 loss (89.3%), in contrast. No significant heterogeneity in PD-L1 expression was observed across the MutSα-deficient subgroups. Conclusions: MMRd endometrial cancer heterogeneity is primarily driven by MutSα and MutLα deficiency. MutSα loss associates with higher PD-L1 expression, suggesting a favorable immune profile warranting further study. Isolated MLH1 loss correlates with the lowest PD-L1 and aggressive features. These findings refine biological understanding and provide a hypothesis-generating rationale for biomarker-guided stratification in immunotherapy. Full article

Background/Objectives: Guizhi Fuling Capsule (GZFL), a Traditional Chinese Medicine (TCM) formula, is widely used for primary dysmenorrhea and other blood-stasis gynecological disorders. This study aimed to characterize its material basis, elucidate its multi-component, multi-target mechanism against acute primary dysmenorrhea, and establish an integrated pharmacokinetic-pharmacometabolomic-pharmacodynamic (PK-PM-PD) framework for TCM efficacy evaluation. Methods: GZFL constituents and serum metabolites in an oxytocin-/estradiol-induced rat dysmenorrhea model were characterized by UPLC/Q-TOF-MS. Uterine effects of GZFL-containing serum were assessed ex vivo. The active components of GZFL were screened by Chinmedomics, with candidate targets investigated through network pharmacology, transcriptomics, and molecular docking. In total, 23 pharmacodynamic indicators were integrated by principal component analysis into an Efficacy Index (EI). Correlation analysis between pharmacometabolomic and pharmacodynamic data yielded a Metabolite-Efficacy Index (MEI), evaluated across a 21-day time course. Results: Among 197 constituents characterized in GZFL extract, 136 serum-exposed components were detected, with several key metabolites enriched via biotransformation. GZFL-containing serum bidirectionally regulated uterine contractility toward the control level. Integrated analyses revealed 68 candidate therapeutic targets. GZFL suppressed NF-κB/IKKβ signaling, down-regulated COX-2/iNOS, restored the PGF2α/PGE2 balance, and normalized inflammatory cytokines. Eleven efficacy-associated metabolites correlated with pharmacodynamic recovery were revealed and integrated, with MEI achieving the highest predictive performance among five integration strategies (AUC = 0.9) and robustly tracking the full 21-day disease-recovery trajectory. Conclusions: GZFL attenuates dysmenorrhea through coordinated regulation of inflammation, prostaglandin metabolism, and uterine functional homeostasis, rather than through inhibition of a single target. The PK-PM-PD framework, with EI and MEI, offers a reproducible paradigm for evaluating complex TCM therapies. Full article

Background/Objectives: Interethnic variability significantly influences drug response, highlighting the importance of assessing genomic ancestry for personalized medicine. Although Latin American populations are highly admixed, they remain underrepresented in pharmacogenetic research. This study examined the association between genomic ancestry, CYP450 genetic variation, and metabolic phenotypes in Ecuadorian (ECU) and Nicaraguan (NIC) populations. Methods: A total of 236 healthy volunteers were studied. Genomic ancestry was evaluated using 83 ancestry-informative markers comprising European (EUR), Native American (NAT), and African (AFR) ancestral components. Genetic variants in CYP2D6, CYP2C9, CYP2C19, CYP3A4, and CYP1A2 were genotyped using TaqMan^® assays. Enzyme metabolic capacity was assessed using the CEIBA cocktail-based on five test drugs. and expressed as the logarithm of the metabolic ratio (logMR). Results: The combined genomic ancestry composition was 48.16% NAT, 40.40% EUR and 11.40% AFR. EUR ancestry was associated with CYP2C9*2 (adjusted p- =0.049), CYP2C19*2 (adjusted p = 0.029), and CYP2D6*41 (adjusted p = 0.010) alleles. AFR ancestry was associated with CYP2D6*17 (adjusted p = 0.001) and CYP2D6*4 (adjusted p = 0.132) alleles. Regarding genotype-predicted metabolic phenotypes, EUR ancestry was associated with CYP2C9 gIM (adjusted p = 0.033), whereas AFR ancestry with CYP2D6 gIM (adjusted p = 0.047) and gPM (adjusted p = 0.047). Furthermore, ancestry-related differences in measured metabolic capacity were observed: AFR ancestry was associated with higher logMR values for, CYP1A2, and CYP3A4, while EUR ancestry was associated with higher logMR values for CYP2C9. Overall, substantial overlap in measured metabolic activity (logMR) was observed across genotype-predicted groups for all genes, although some differences were identified. Conclusions: Genomic ancestry was associated with the distribution of CYP450 alleles and genotype-predicted metabolizer categories in Ecuadorian and Nicaraguan populations. However, both ancestry and genotype-based classifications showed limited ability to explain interindividual variability in measured metabolic activity, highlighting the complexity of CYP450 phenotype prediction. Full article

Background: Interindividual variability in sertraline pharmacokinetics remains substantial, but the relative contributions of pharmacogenetic (PGx), demographic, clinical, and prescribing determinants have not been comprehensively evaluated in routine clinical practice. The study aimed to evaluate PGx, demographic, clinical, and prescribing determinants of sertraline plasma concentrations, sertraline plasma concentration-to-dose ratio (C/D), and non-dose-normalized sertraline-to-norsertraline metabolic ratio (MR), with log₁₀-transformed dose-normalized sertraline/norsertraline metabolic ratio (LogMR_DN) and log₁₀-transformed MR (LogMR) evaluated as exploratory derived pharmacokinetic outcomes in real-world clinical settings. Methods: This prospective real-world PGx study included 153 patients receiving sertraline therapy who participated in the MedeA clinical PGx implementation program. Associations of genotype-predicted phenotypes for CYP2C19, CYP2B6, CYP2D6, CYP2C9, and CYP3A4, CYP2C:TG haplotype status, demographic, clinical, and prescribing characteristics with sertraline concentration, C/D, MR, and LogMR_DN were evaluated using univariable analyses. Separate multivariable linear regression models were fitted for these outcomes and LogMR using 140 complete cases. Results: CYP2C19 gPM status was associated with higher sertraline concentrations (β = 72.10, 95% CI: 21.36–122.84; p = 0.01). Although a higher sertraline C/D ratio was observed in the conventional multivariable model with CYP2C19 gPM (β = 1.46, 95% CI: 0.85–2.08; p < 0.001), this association was not retained after HC3 heteroscedasticity-robust standard errors were applied (β = 1.46, 95% CI: −0.09–3.02; p = 0.07). In a small exploratory subgroup analysis, patients with combined CYP2C19 and CYP2B6 poor-metabolizer status (n = 2) had sertraline concentrations exceeding 150 ng/mL. Conclusions: The study findings provide further insights into the relevance of CYP2C19 in sertraline pharmacokinetics. Full article

Embedding an evolutionary algorithm (EA) into control structures offers an effective solution for specific control problems. Often, it predicts the best control values using a process model (PM). The primary limitation is its high computational time. Our work addresses optimal control problems (OCPs) with a final cost, using receding horizon control (RHC) with an EA as a predictor. This work is a continuation of a previous article, in which the EA predictor was replaced with a multilinear regression-based predictor. Our objective is to propose a predictor based on regression neural networks (RNNs) that emulates the behavior of the (EA, PM) couple. A number of closed-loop simulations using the existing EA controller produce sequences of optimal control values and corresponding state values, which are stored in a data structure. Datasets for each sampling period are derived from these data and are used to train RNN objects employing a unique RNN model. The model, which is an “optimizable” RNN plus the list of hyperparameters preset before optimization, is determined after a thorough analysis of possible candidates using a MATLAB R2025b application. The presented method of constructing an RNN predictor is the main contribution. Algorithms for (a) constructing the sequence of RNN objects and (b) simulating the closed loop are also proposed. A case study illustrates our method. The RNN predictor successfully emulated the (EA, PM) couple: (a) the control-loop dynamics were nearly identical; (b) the performance indices were essentially the same; and (c) the execution time of the controller significantly decreased from 38 to 0.054 s, demonstrating that RHC can be applied more broadly. Full article

Accurate and continuous air quality monitoring is essential for sustainable urban management, environmental protection, and public health. However, dense networks of low-cost sensors frequently suffer from missing observations caused by communication failures, sensor malfunctions, or maintenance operations. This paper proposes a hybrid graph convolutional network–temporal convolutional network (GCN-TCN) framework for imputing missing ${\mathrm{PM}}_{2.5}$ measurements in dense sensor networks. The model utilizes spatial relationships between neighboring monitoring stations and temporal dependencies within sensor time series. The proposed approach was evaluated using data from a network of 20 sensors deployed across the academic campus area. Three representative missing-data scenarios were considered, including isolated missing observations, continuous missing sequences, and a hybrid combination of both patterns. The proposed model achieved the lowest reconstruction errors and the highest coefficient of determination among the evaluated methods (${\mathrm{R}}^2\approx$ 0.91–0.93). Standard GCN and TCN networks achieved lower ${\mathrm{R}}^2$ values (∼0.81–0.82 and ∼0.71–0.80, respectively), while recurrent models and classical statistical methods performed substantially worse (${\mathrm{R}}^2\approx 0.6$). These findings indicate that integrating graph-based spatial learning with temporal convolution robustly reconstructs incomplete environmental observations, improving the reliability of low-cost sensor systems for sustainable air quality monitoring and management. Full article

Exposure to atmospheric particulate matter (PM) is a major public-health concern, in part because PM can perturb cellular redox homeostasis. This study evaluates an in vitro A549/DCFH-DA approach using repeated fluorescence readouts to assess PM2.5-induced oxidative activity. Untreated and assay-specific controls were combined with zymosan and NIST Standard Reference Material^® 2584 suspended in PBS, and fluorescence was monitored at multiple readout times over a 15 min–6 h window. Method performance was characterized using the coefficient of variation (CV) and signal-to-noise ratio (SNR). A dedicated three-concentration SRM 2584 series (0.02, 0.05 and 0.10 mg mL⁻¹) further showed readout-dependent concentration behaviour: at 60 min the untreated-control-corrected mean response increased across the tested concentrations and followed an approximate descriptive linear trend (R² = 0.90), whereas earlier readouts were non-monotonic. Substrate-related effects were examined using paired PTFE and quartz filters. Among the eight matched PTFE–quartz pairs included in the regression analysis, zero-intercept fits showed slopes close to unity for both mass- and air-volume-normalized responses (0.90 and 0.99, respectively; R² ≈ 0.99), demonstrating strong proportional agreement within this comparison set; the limited number of pairs does not support universal substrate interchangeability. Application to chemically characterized field PM2.5 samples from an urban-background site and a high-altitude site showed that DCFH-DA fluorescence did not track PM mass alone and is interpreted in terms of exploratory associations with particle composition, rather than causal effects of individual constituents. Taken together, these findings support the use of the method-performance-characterized workflow for assessing oxidative responses to field-collected PM2.5 across multiple readout times and for investigating their associations with particle chemical characteristics. Full article