Search Results (155)

The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R², RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication. Full article

To address the challenges of low land use efficiency, soil degradation, and high management costs in Ilex asprella cultivation, this study established an I. asprella–Grona styracifolia intercropping system and systematically evaluated its effects on soil nutrient cycling, microbial communities, and crop growth. Field experiments were conducted in Yunfu City, Guangdong Province, with monoculture (LCK for I. asprella, DCK for G. styracifolia) and three intercropping densities (HDT, LDT, MDT). Combining 16S rRNA sequencing and metagenomics, we analyzed the functional profile of the rhizosphere microbiome. The results showed that intercropping significantly increased the biomass of G. styracifolia, with the medium-density (MDT) treatment increasing plant length and fresh weight by 41.2% and 2.4 times, respectively, compared to monoculture. However, high-density intercropping suppressed the accumulation of medicinal compounds. In terms of soil properties, intercropping significantly enhanced soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) in the rhizosphere of both plants. Specifically, AN in the I. asprella rhizosphere increased by 18.9%. Soil urease and acid phosphatase activities were also elevated, while pH decreased. Microbial analysis revealed that intercropping reshaped the rhizosphere microbial community structure, significantly increased the Shannon diversity index of bacteria in the G. styracifolia rhizosphere, and enhanced the complexity of the microbial co-occurrence network. Metagenomic analysis further confirmed that intercropping enriched functional genes related to carbon fixation, nitrogen cycling (nitrogen fixation, assimilatory nitrate reduction), and organic phosphorus mineralization (the phoD gene), thereby driving the transformation and availability of soil nutrients. These findings demonstrate that the I. asprella–G. styracifolia intercropping system, particularly at medium density, effectively improves soil fertility and land use efficiency by regulating rhizosphere microbial functions, providing a theoretical basis for the sustainable ecological cultivation of I. asprella. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article

Background/Objectives: Conventional next-generation sequencing (NGS) workflows often require more than two weeks to complete, delaying treatment decisions and limiting access to precision oncology in community settings. This report aimed to demonstrate the feasibility of performing rapid, comprehensive cell-free DNA (cfDNA)-based genomic profiling by introducing a fully automated NGS workflow in a community hospital environment. Case Presentation: A postoperative patient with pancreatic ductal adenocarcinoma and liver metastasis underwent cfDNA-based liquid biopsy using plasma collected in PAXgene^® Blood ccfDNA Tubes. Gene analysis was performed using the Oncomine Precision Assay GX5 on the Ion Torrent Genexus™ System (Thermo Fisher Scientific). Three pathogenic hotspot mutations—KRAS G12R, TP53 M246I/M246K, and GNA11—and one copy number gain in PIK3CA were identified, whereas no variants were detected in a healthy volunteer control. The total turnaround time from plasma separation to report generation was approximately 27 h, requiring only 40 min of total hands-on time. Discussion: This rapid, automated workflow enabled comprehensive cfDNA analysis within a clinically practical timeframe, overcoming key limitations of conventional multi-step NGS workflows that typically require external sample shipment and specialized personnel. The results confirm the technical feasibility of conducting high-quality molecular testing in a regional hospital setting. Conclusions: This report demonstrates that fully automated cfDNA-based NGS can achieve clinically meaningful genomic profiling within 27 h in a community hospital. This advancement addresses the time and cost barriers of traditional NGS analysis and represents a significant step toward promoting precision medicine in community healthcare. Full article

Benthic bacterial communities play a critical role in nutrient cycling and are highly sensitive to environmental pollution. This study aimed to investigate the physiological, compositional and functional responses of bacterial communities across a range of carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) gradients in sediments from Lake Villarrica, Chile. Sediment samples were collected from 5 sites representing a gradient of nutrient pressure from the lake basin (NL < PuB < PoP < SL < VB). Nutrient forms (TC, TN, TP, TS, and OM) were chemically quantified. Community function was assessed via community-level physiological profiles (CLPPs) using Biolog^® EcoPlates (C substrates), PM3B (N substrates), and PM4A (P and S substrates). Function and composition were assessed based on total bacterial and functional nutrient-cycling gene abundances (16Sr RNA, chiA, mcrA, nifH, amoA, nosZ, phoD, pqqC, soxB, dsrA) using qPCR and 16S rRNA metabarcoding, respectively. In general, the CLPPs were higher for C substrates, followed by P, S, and N substrates, with metabolism of organic forms of these nutrients preferential, and P-cycling genes were the most abundant in the lake. Spatially, the most nutrient-enriched site (VB) showed a significantly (p ≤ 0.05) higher nutrient content (e.g., 5.4% TC, 0.54% TN, 1302.8 mg kg⁻¹ TP and 854.1 mg kg⁻¹ TS) and total bacterial abundance (2.9 × 10¹¹ gene copy g⁻¹ dw sediment) but displayed lower CLPPs (from 0.63 to 1.02 AWCD) and nutrient-cycling gene abundances (e.g., 9.1 × 10¹, 2.7 × 10³, 3.6 × 10³ and 4.7 × 10³ gene copy g⁻¹ dw sediment for chiaA, nifH, phoD and dsrA, respectively) compared to the less nutrient-enriched sites (e.g., NL). The bacterial community composition shifted accordingly, with Bacillota enriched in VB and Planctomycetota occurring more frequently in less nutrient-exposed sites. Functional prediction analysis revealed enhanced methanotrophy and sulfate respiration in nutrient-rich sediments, whereas nitrification and organic P (P_o) mineralization dominated in less impacted areas. The results demonstrate that nutrient enrichment constrains bacterial functional diversity in Lake Villarrica and, so, may be useful indicators of environmental stress to be considered in pollution monitoring programmes. Full article

Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes. Full article

The Slit2/Robo signaling pathway acts as a tumor suppressor in various cancers. This study identified an 8-amino acid peptide, LT1-3, derived from the Slit2 LamG domain, and demonstrated its ability to inhibit lung cancer cell proliferation and invasion independently of Robo receptors. Notably, LT1-3 was non-toxic to normal cells (Beas-2B, MRC5, and HUVECs). Combination treatment of LT1-3 and cisplatin synergistically inhibited the proliferation of lung cancer cells (CL1-5, A549, H1355, H460, H23, H661), but had no inhibitory effect on H1299 and H1975. Furthermore, combination therapy prolonged the median survival of tumor-bearing immunodeficient nude mice from 27.5 days (control) to 37.5 days (LT1-3 or cisplatin) and further to 47.5 days (LT1-3/cisplatin combination). The tumor suppressor TP53 positively influences LT1-3-mediated proliferation inhibition, while MAPK8 (JNK1) and PRKACA (PKA) have been identified as negative regulators. With the exception of the p53R273 variants, most TP53 mutants retained their function in this context. The p53 reactivator APR-246 restores sensitivity of p53R273H-expressing cells to LT1-3. JNK inhibition sensitizes p53-deficient or p53R273H-expressing cells to LT1-3-mediated proliferation inhibition. LT1-3, alone or in combination with a JNK inhibitor, enhances cisplatin efficacy, even in the presence of p53 mutations. Therefore, LT1-3 possesses multifunctional antitumor properties, directly inhibiting tumor cells and enhancing the efficacy of cisplatin, without causing toxicity to normal cells. Combining LT1-3 with cisplatin holds promise as a first-line therapy for lung cancer, while LT1-3 alone may be suitable for maintenance therapy. Full article

Soil organic matter (SOM) is a key component of nutrient cycling and soil fertility in terrestrial ecosystems. SOM is of great significance to the stability of terrestrial ecosystems and the improvement of soil productivity; to further exert its role, it is first necessary to clarify its actual distribution and occurrence status in specific regions. Under the combined impacts of intensive agriculture, unreasonable farming practices, and climate change, the SOM content in the Songnen Plain is showing a degradation trend, posing multiple stresses on its soil ecosystem functions. This study aims to systematically track the dynamic changes of SOM in the Songnen Plain, assess its spatiotemporal evolution characteristics, and reveal its driving mechanisms. A total of 113 representative soil profiles were selected in 2023; standardized excavation and sampling procedures were employed in the Songnen Plain. Soil pH, SOM, total nitrogen (TN), total phosphorus (TP), total potassium (TK), particle size (PSD), texture, and Munsell soil colors of samples were determined. Temporal variation characteristics, as well as horizontal and vertical spatial distribution patterns, in SOM content in the Songnen Plain were assayed. Structural equation modeling (SEM), together with freeze–thaw of soil and soil color mechanism analyses, was applied to reveal the spatiotemporal dynamics and driving mechanisms of SOM. The result indicated that the distribution pattern of SOM content in horizontal space shows higher levels in the northeastern region and lower levels in the southwestern region, and decreased with increasing soil depth. SEM analysis indicated that TN and PSD were the main positive factors, whereas bulk density exerted a dominant negative effect. The ranking of contribution rates is TN > TK > TP > PSD > annual average temperature > annual precipitation > bulk density. Mechanistic analysis revealed a significant negative correlation between SOM content and R, G, B values, with soil color intensity serving as a visual indicator of SOM content. Freeze–thaw thickness of soil was positively correlated with SOM content. These findings provide a scientific basis for soil fertility management and ecological conservation in cold regions. Full article

Understanding heavy metal behavior in arid saline soils is critical for phytoremediation in degraded lands. This study investigated metal distribution and plant enrichment in the Tarim Basin using 323 soil and 55 plant samples (Populus euphratica, Tamarix ramosissima, cotton, jujube). Analyses included redundancy analysis (RDA) and bioconcentration factor (BCF) assessments. Key findings reveal that elevated salinity (total salts, TS > 200 g/kg) and alkalinity (pH > 8.5) immobilized As, Cd, Cu, and Zn. Precipitation and competitive leaching reduced metal mobility by 42–68%. Plant enrichment strategies diverged significantly: P. euphratica hyperaccumulated Cd (BCF = 1.59) and Zn (BCF = 2.41), while T. ramosissima accumulated As and Pb (BCF > 0.05). Conversely, cotton posed Hg transfer risks (BCF = 2.15), and jujube approached Cd safety thresholds in phosphorus-rich soils. RDA indicated that pH and total salinity (TS) jointly suppressed metal bioavailability, explaining 57.6% of variance. Total phosphorus (TP) and soil organic carbon (SOC) enhanced metal availability (36.8% variance), with notable TP-Cd synergy (Pearson’s r = 0.42). We propose a dual-threshold management framework: (1) leveraging salinity–alkalinity suppression (TS > 200 g/kg + pH > 8.5) for natural immobilization; and (2) implementing TP control (TP > 0.8 g/kg) to mitigate crop Cd risks. P. euphratica demonstrates targeted phytoremediation potential for degraded saline agricultural systems. This framework guides practical management by spatially delineating zones for natural immobilization versus targeted remediation (e.g., P. euphratica planting in Cd/Zn hotspots) and implementing phosphorus control in high-risk croplands. Full article

Infusions of the leaves of Ribes magellanicum (Grossulariaceae) are used as a digestive in southernmost South America. This work aimed to assess the composition and activity of infusions and MeOH:H₂O 7:3 extracts of R. magellanicum leaves on enzymes related to metabolic syndrome (α-glucosidase, α-amylase, and pancreatic lipase), as well as their antioxidant capacity. Samples from a longitudinal gradient from central southern Chile to the islands in the Beagle Channel were investigated. Lyophilized infusions and extracts were used for all determinations, including inhibition of the selected enzymes, total phenolic (TP), total flavonoid (TF), total procyanidins (TPC), and antioxidant capacity (DPPH, FRAP, TEAC, and ORAC). The composition of the samples was assessed by HPLC-DAD. Some 99 compounds were tentatively identified by HPLC-MSⁿ. The main phenolics were quantified using calibration curves with reference compounds. Relevant differences exist in the ratio of constituents in infusions compared to hydroalcoholic extracts. The samples were inactive towards α-amylase and pancreatic lipase at 100 and 50 µg/mL, respectively. Assay-guided isolation of α-glucosidase inhibitors led to fractions with high activity (IC₅₀: 0.02–0.05 µg/mL). The strong inhibition of α-glucosidase and antioxidant capacity of the infusion and extracts of R. magellanicum leaves support its traditional use in southern Patagonia. Full article

Somatic mutations in KRAS and TP53 are among the most common genetic alterations in pancreatic ductal adenocarcinoma (PDAC). Advances in PCR-based technologies now enable the detection of these mutations in tumor tissue and cell-free DNA (cfDNA), providing a minimally invasive approach to assess tumor burden. However, in resectable PDAC, circulating tumor DNA (ctDNA) may represent less than 0.1% of total cfDNA, requiring highly sensitive detection methods. The aim of our study was to assess two PCR-based assays—competitive allele-specific PCR (castPCR) and digital PCR (dPCR)—for detecting selected somatic mutations in tumor tissue, cfDNA, and extracellular vesicle-associated DNA (EV-DNA) from plasma. Matched primary tumor and preoperative plasma samples were collected from 50 patients undergoing upfront resection for PDAC. CastPCR was used for detecting selected KRAS, TP53, SMAD4, and CDKN2A mutations in tumor DNA. Additionally, dPCR was used to analyze KRAS and TP53 mutations in tumor DNA as well as cfDNA and EV-DNA. The concordance between both platforms was 71.4% for KRAS p.G12D and 58.3% for the analysis of TP53 p.R273H mutations in tumor tissue. However, dPCR detected these mutations in an additional 28.6% and 39.6% of samples, respectively. In cfDNA, dPCR identified KRAS p.G12D in 10.2% and TP53 p.R273H in 2.0% of samples. Mutation detection in EV-DNA was limited by low DNA yield. Both platforms proved effective for tumor DNA analysis, with dPCR offering greater sensitivity. Somatic mutation detection from liquid biopsy using dPCR further supports its potential utility in the preoperative setting. Full article

Activation of TP53 signaling during ribosome biogenesis is an essential part of erythroid development, whereas the pathologic activation of TP53 in ribosomopathies such as Diamond-Blackfan anemia (DBA) and del (5q) myelodysplastic syndrome (MDS) prevents the normal expansion of erythroid precursors. TP53 can also be linked to the pathogenesis of DBA and MDS via ferroptosis, a form of iron-mediated cell death propagated by excess polyunsaturated fatty acid-containing oxidizable phospholipids and loss of lipid peroxide repair capacity. The primary objective of this work was to establish how overexpression and mutation of the TP53 gene influences lipid composition, erythroid differentiation, and ferroptosis sensitivity in K-562 cells, an in vitro model for studying erythropoiesis. Employing a reverse genetics approach, we generated four isogenic cell lines that either lacked functional TP53 expression, expressed wild-type (WT) TP53, or expressed one of the two most common TP53 mutation types, R175H or R282W. We then utilized non-targeted lipidomics to quantify and identify changes in specific lipid species that occur with induction of WT and mutant TP53 expression. We also analyzed differences in gene expression, ferroptosis sensitivity, and hemoglobinization by qPCR, CCK-8 cytotoxicity assay, and o-dianisidine staining, respectively. The abundance of 337 distinct lipid species was impacted by induction of WT TP53 expression compared to K-562 cells expressing a nonfunctional P53 protein. Yet only 17 lipid compounds were differentially impacted between cells expressing WT TP53 and either of the mutant TP53 genes tested. Similarly, while the TP53 null K-562 cells displayed modest sensitivity to ferroptosis, cells expressing both WT and mutant TP53 genes were remarkably resistant to ferroptosis. However, terminal differentiation and hemoglobinization were significantly impacted in R175H mutant TP53-expressing K-562 cells. Findings from this work provide novel insights into the role of TP53 in lipid metabolism and terminal erythropoiesis. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article

Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive malignancies, with dismal survival rates. Cannabinoids have shown anticancer properties in various cancers, including PDAC. This study aimed to evaluate the anticancer effects of cannabinoids, individually and in combination, and to elucidate their mechanisms of action in a murine PDAC model (KPC mice, KRASWT/G12D/TP53WT/R172H/Pdx1-Cre+/+) that mimics human disease. Additionally, the study explored the potential link between cannabinoid action, gut microbiota modulation, and bile acid (BA) metabolism. PDAC cell lines and KPC mice were treated with delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), either as monotherapy or in combination. Faecal pellets, caecal contents, plasma, and tissues were collected at the survival endpoint for analysis. BA profiling was performed using mass spectrometry, and the faecal microbiota was characterised by sequencing the V3-V4 region of the 16S rRNA gene. While CBD and THC synergistically reduced cell viability in PDAC cell lines, only CBD monotherapy improved survival in KPC mice. Extended survival with CBD was accompanied by changes in gut microbiota composition and BA metabolism, suggesting a possible association. Notably, the effects of CBD were different from those observed with THC alone or in combination with CBD. The study highlights a distinct role for CBD in altering BA profiles, suggesting these changes may predict responses to cannabidiol in PDAC models. Furthermore, the findings propose that targeting BA metabolism could offer a novel therapeutic strategy for PDAC. Full article

Iopamidol (IPM), as a typical recalcitrant emerging pollutant and precursor of iodinated disinfection by-products (I-DBPs), is unsuccessfully removed by conventional wastewater treatment processes. This study comprehensively evaluated the ozone/peracetic acid (O₃/PAA) process for IPM degradation, focusing on degradation kinetics, environmental impacts, transformation products, ecotoxicity, disinfection byproducts (DBPs), and microbial inactivation. The O₃/PAA system synergistically activates PAA via O₃ to generate hydroxyl radicals (^•OH) and organic radicals (CH₃COO^• and CH₃CO(O)O^•), achieving an IPM degradation rate constant of 0.10 min⁻¹, which was significantly higher than individual O₃ or PAA treatments. The degradation efficiency of IPM in the O₃/PAA system exhibited a positive correlation with solution pH, achieving a maximum degradation rate constant of 0.23 min⁻¹ under alkaline conditions (pH 9.0). Furthermore, the process demonstrated strong resistance to interference from coexisting anions, maintaining robust IPM removal efficiency in the presence of common aqueous matrix constituents. Furthermore, quenching experiments revealed ^•OH dominated IPM degradation in O₃/PAA system, while the direct oxidation by O₃ and R-O^• played secondary roles. Additionally, based on transformation products (TPs) identification and ECOSAR predictions, the primary degradation pathways were elucidated and the potential ecotoxicity of TPs was systematically assessed. DBPs analysis after chlorination revealed that the O₃/PAA (2.5:3) system achieved the lowest total DBPs concentration (99.88 μg/L), representing a 71.5% reduction compared to PAA alone. Amongst, dichloroacetamide (DCAM) dominated the DBPs profile, comprising > 60% of total species. Furthermore, the O₃/PAA process achieved rapid 5–6 log reductions of E. coli. and S. aureus within 3 min. These results highlight the dual advantages of O₃/PAA in effective disinfection and byproduct control, supporting its application in sustainable wastewater treatment. Full article