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PubMed2026

Leveraging environmental applications and risks of coal gangue: A critical review on authigenic inorganic heavy metals, organic contaminants, and the removal of exogenetic contaminants.

Coal gangue (CG) as bulk solid waste has seriously threatened the ecosystem. Therefore, identifying the key risk drivers and exploring feasible disposal methods for CG are essential for developing a sustainable strategy. However, there is currently a lack of comprehensive information that balances the contamination risks with the valuable constituents present in CG, which hinders its full potential for sustainable use without negative environmental impacts. Given the complex composition and associated risks, we propose that addressing the critical properties related to contamination is crucial for the efficient utilization of CG. On this premise, we summarized several practical resource pathways (ecological multifunctional materials, extraction of rare elements, and soil additives) that are more favorable for sustainable development relative to conventional disposals. Meanwhile, we also propose that coupling disposals could intensely reduce CG's environmental footprints and capital costs. Consequently, regardless of the number of challenges to be solved, we believe the CG has broad application prospects, and we hope this review will promote the conversion of CG into an asset with lower ecological and social impacts.

Metals, Heavy↗

PubMed2026

Efficacy of prescription-eligible digital health applications for depression and generalized anxiety disorder in Germany: a systematic review and meta-analysis.

In Germany, prescription-eligible digital mental health applications (DiGA) were introduced in 2020 as promising interventions to address, among others, depression and anxiety disorders, two of the most prevalent mental health conditions worldwide. Despite growing interest in DiGAs, their overall efficacy remains uncertain. This study aimed to systematically evaluate and quantify the efficacy of prescription-eligible digital interventions for depression and generalized anxiety disorder by synthesizing evidence from randomized controlled trials (19 trials; total N = 4,078; pooled mean age = 38.7 years, SD = 12.1). Here we show that prescription-eligible digital applications for depression and generalized anxiety disorder reduce symptom severity compared with control conditions. For depression, effects were observed both immediately after the intervention (number of apps = 5; k = 17; SMD = - 0.49; 95% CI - 0.65 to - 0.32) and at follow-up (number of apps = 1; k = 4; SMD = - 0.35; 95% CI - 0.46 to - 0.29), while evidence for generalized anxiety disorder was limited due to a small number of available studies (number of studies = 2). These findings support the integration of evidence-based digital tools into mental health treatment strategies in Germany. However, the available evidence is currently dominated by a small number of applications, particularly Deprexis, and should therefore not be interpreted as equally representative of all DiGAs currently listed for depression in Germany. The findings also highlight methodological limitations of current research and underscore the need for real-world evaluations, which address not only efficacy but also the effectiveness, content, quality and implementation.

Generalized Anxiety Disorder↗

PubMed2026

XsiAMT1.1a was identified as a novel ammonium uptake functional gene and its overexpression combined with GA4 application significantly increased yield in Arabidopsis thaliana.

Nitrogen (N) is a key limiting factor for plant yield. Ammonium is one of the main N forms absorbed by plants. Overexpression of ammonium uptake functional genes, such as ammonium transporter (AMT), can increase yield. However, the AMTs reported to enhance yield significantly is still limited. No researches have focused on the effect of overexpressing AMT combined with hormone application on yield improvement. In this study, we first investigated the role of XsiAMT1.1a, a potential ammonium uptake functional gene in an ammonium preference plant Xanthium sibiricum, in ammonium uptake by the analysis of bioinformatics, gene expression and subcellular localization, and the determination of ammonium uptake rate in endogenous silencing and heterologous overexpression plants. Subsequently, the effect of XsiAMT1.1a overexpression combined with hormone application on yield increase was further investigated in model plant Arabidopsis thaliana. Our results showed that XsiAMT1.1a shared the same conserved domains with AtAMT1 subfamily members and localized on the plasma membrane. XsiAMT1.1a was induced by N deficiency and highly expressed during the reproductive period. XsiAMT1.1a endogenous silencing and heterologous overexpression significantly decreased and increased ammonium uptake rates in X. sibiricum and A. thaliana, respectively. Overexpression of XsiAMT1.1a significantly improved total N accumulation, biomass and yield in A. thaliana, while XsiAMT1.1a overexpression combined with GA4 application had a stronger promoting effect on the above indicators. Our research identified a novel ammonium uptake functional gene, XsiAMT1.1a, and provided a new yield-increasing strategy which was verified in A. thaliana.

Arabidopsis↗

PubMed2026

A clinical study on the efficacy of rectal administration of Tongfu Qinghua decoction combined with external application of Ruyi Jinhuang powder in treating acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a common acute abdominal disease with high mortality in moderate and severe cases. Integrated Chinese and Western medicine therapy has promising clinical application prospects. OBJECTIVES: This study investigated the efficacy and safety of Tongfu Qinghua decoction enema combined with Ruyi Jinhuang powder external application for AP and its therapeutic effects across different age groups. METHODS: A total of 100 AP patients from October 2023 to August 2025 were randomly divided into observation and control groups (50 cases each). The control group received conventional Western medicine and the observation group received additional combined Chinese medicine therapy. Outcomes including hospital stay, symptom relief, inflammatory and pancreatic injury markers, clinical efficacy and adverse reactions were compared, with subgroup analysis of patients aged 18-40, 41-60 and 61-75 years. RESULTS: The observation group had significantly shorter hospital stay, faster symptom relief and gastrointestinal recovery (P<0.05). Post-treatment inflammatory and pancreatic markers improved significantly and the total effective rate was higher (P<0.05), with no significant difference in adverse reactions (P>0.05). Benefits were consistent across all age subgroups, with younger patients recovering faster and elderly patients still achieving significant improvement. CONCLUSION: This combined therapy is effective and safe for AP patients aged 18-75 years, significantly improving clinical outcomes and worthy of clinical promotion.

Humans↗

PubMed2026

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

Robotic Surgical Procedures↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Evaluation of the effects of domestic tomato processing on biopesticide residue using natural deep eutectic solvents (NADES) extractions.

The present study evaluated the fate of fourteen botanical biopesticides in processed tomato samples. Various processing methods were employed, including washing, dehydration, and the preparation of juice and sauce. The extraction was performed using more sustainable techniques, aimed at minimizing the environmental impact of conventional organic solvents by substituting them with natural deep eutectic solvents (NADES). Solid-liquid extraction (SLE) and dispersive liquid-liquid microextraction with solidification of floating organic drop (DLLME-SFOD) were utilized for solid and liquid tomato samples, respectively. The NADES used was choline chloride:2,3-butanediol (ChClBt) at a 1:4 molar ratio for both techniques, resulting in recovery values ranging from 69.2 to 106.2% for SLE, and extraction efficiencies reaching up to 46.2% for DLLME-SFOD. The impact of these processes was evaluated employing the processing factor (PF), yielding PF values of less than 1 in all cases. Compounds as pyrethrins, azadirachtin, and rotenone persisted after processing, posing a potential consumer risk.

Solanum lycopersicum↗

PubMed2026

Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.

CRISPR-Cas Systems↗

PubMed2026

Applications and outcomes of virtual reality in inpatient psychiatry: A systematic review.

BACKGROUND: Virtual reality (VR) has been widely used in outpatient psychiatric services and has demonstrated benefits across several clinical diagnoses, but its use and effects in inpatient settings remain to be explored. This systematic review aimed to examine the use of VR during psychiatric hospitalization, including types of VR applications, barriers and facilitators of implementation, and effects on various outcomes. METHODS: The review was registered in PROSPERO (#CRD42023446524). Following PRISMA guidelines, databases (Ovid, SciVerse, Web of Science, Cochrane Library, ProQuest, and WorldCat) were searched from 1983 to 2025 using keywords related to VR and psychiatric disorders. Studies involving the use of VR with psychiatric inpatients (≥85%) were included. Descriptive statistics and narrative syntheses were used to summarize findings. Study quality was assessed with the Mixed Methods Appraisal Tool. RESULTS: After full-text screening, 37 studies (N = 1,004) met inclusion criteria. VR was used for both assessment and intervention, with cognitive-behavioral therapy/exposure (35%) and assessment (24%) being the most frequently used. VR use in inpatient units appeared feasible, acceptable, and safe for inpatients and clinicians, though findings remain preliminary. Several facilitators (e.g. adequate staff training and supervision) and common barriers (e.g. technical difficulties and limited resources) were identified. The most consistent improvements were observed in clinical symptoms (e.g. anxiety) compared with psychosocial, cognitive, and physiological outcomes. CONCLUSIONS: These findings suggest that inpatient settings represent a promising, yet understudied context for VR-based assessments and interventions. High-quality trials and systematic reporting of implementation are needed in future studies to inform research and clinical practice.

Humans↗

PubMed2026

Intraskeletal Variation in Cortical Bone Quantity in a Medieval Italian Sample: A Multivariate Exploratory Approach.

Bioarcheologists interpret skeletal health by examining variability within and between individuals. Studies of bone loss have generated contradictory and conflicting results regarding the onset and severity of age-related bone loss on a global and temporal scale, perhaps due to mismatched methodologies. Intraskeletal comparisons of bone tissue prove challenging precisely because of heterogeneous baselines in quantity and remodeling of cortical bone throughout the skeleton, as well as evolutionary histories and environmental impacts on growth and development. Here we analyze cortical bone indicators from the rib, metacarpal, and femoral cortical bone in a subset of individuals (n = 72) regions from the medieval Italian archaeological site of Pieve di Pava. To facilitate intraskeletal comparisons across elements with different biological baselines, we standardize cortical bone parameters using z-scores. Variation in relative intraskeletal cortical bone was assessed using accessible multivariate methods (principal component analysis and hierarchical cluster analysis). Results suggest an association between femoral and metacarpal cortical bone values, with stochastic trends in metacarpal and femoral relative bone quantity in relation to the rib bone quantity at the sample level. Our study demonstrates that while intraskeletal analyses are challenging, they are made more robust by synthesizing multivariate methods alongside exploratory data analysis (EDA) methods to tack between sample-level and individual-level scales and variability. Ultimately, we advocate for leveraging multivariate techniques not as a final step, but rather as a means of generating new hypotheses and challenging tendencies to a priori establish typological groups in the research process.

Skeleton↗

PubMed2026

Getting to the Core of the Matter-Assessing the Role of Replication in Metabarcoding-Based sedaDNA.

Replication is central to most experimental and sampling designs, increasing inferential power and capturing fine-scale data heterogeneity. However, its importance remains poorly evaluated in some ecological and evolutionary settings. This is the case of metabarcoding studies using DNA recovered from sedimentary archives, in which biological signals integrate ecological information through depositional and burial processes, yet are commonly inferred from a single sediment core per site. Here, we evaluated the effect of different types of replication using sedimentary DNA metabarcoding data from two genetic markers (mitochondrial COI and nuclear 18S) using a nested sampling design. The design included three intertidal sites, three spatially separated sediment cores per site (biological replicates), two sediment horizons per core, and eight PCR (technical) replicates per sediment sample. Variance partitioning showed that site identity and sediment age group together explained > 70% of the variation in beta diversity, indicating that among-site spatial and stratigraphic differences were the dominant drivers of community composition. PERMANOVA likewise identified non-significant effects of biological replication. Among PCR replicates from the same sediment sample, richness varied substantially, whereas Shannon diversity was more consistent. Despite this variability, differences in community composition among technical replicates remained smaller than those associated with biological replication or site identity, indicating a limited influence on broader ecological patterns. Community composition was highly similar among replicate cores within sites, consistent with stratigraphic coherence. These results indicate limited within-site heterogeneity and suggest that, under stratigraphically coherent conditions, increasing biological replication may provide little additional information, whereas enhancing technical replication and stratigraphic resolution can improve ecological inference from sedimentary DNA metabarcoding datasets.

DNA Barcoding, Taxonomic↗

PubMed2025

Application of causal discovery of factors driving dissolved oxygen in estuarine environments.

Dissolved oxygen (DO) concentrations in estuarine bottom waters are a manifestation of multiple, interacting physical and biogeochemical processes, yet identifying their independent contributions remains challenging. Here, we analyze monthly water quality monitoring data from eight stations across Long Island Sound from 1994 to 2022 using a causal discovery framework (PCMCI+) and transformation of forcing variables. Our goal is to identify and isolate variables that causally influence bottom DO and improve predictive models by minimizing overfitting and multicollinearity. PCMCI+ reveals surface-layer temperature as the most important and consistent negative driver of bottom DO, followed by stratification. Wind events exhibit only brief relief by advection and mixing, while river discharge shows no direct causal link to DO, making it less influential than previously thought. Biogeochemical variables, including chlorophyll-a (Chl-a), nitrate and nitrite, and particulate carbon, influence DO through both contemporaneous and time-lagged pathways, often with signs that shift depending on the process. The derived models were evaluated by comparing skill scores, mean squared error, and Akaike Information Criterion. Both model types perform well, with coefficient of determination values exceeding 0.90 at multiple stations using only 3-5 predictors. Our analysis reveals that the best causal predictors are surface-layer temperature, stratification, Chl-a, and particle carbon. This approach provides a scalable framework for improving prediction models and understanding the mechanistic links that control the seasonal variability of DO in estuarine systems.

Estuaries↗

PubMed2026

Novel environmental contaminant 6PPD-quinone promotes malignant phenotypes in colorectal cancer cells and identifies candidate response-associated genes.

6PPD-quinone (6PPDQ), an oxidative transformation product of the widely used tire antioxidant 6PPD, is a ubiquitous environmental contaminant with bioaccumulation potential and widespread human exposure. Recent epidemiological evidence indicates a positive association between urinary 6PPDQ levels and colorectal cancer (CRC) risk; however, its biological effects on CRC-related phenotypes and associated molecular responses remain unclear. We integrated bioinformatics analysis, prognostic modeling, molecular docking and dynamics simulations, and in vitro experiments to investigate cellular and molecular responses to 6PPDQ in CRC models. Predicted 6PPDQ targets were intersected with CRC prognosis-related genes from The Cancer Genome Atlas, followed by functional enrichment and LASSO regression to construct a prognostic risk model, with 1-, 3-, and 5-year AUC values of 0.727, 0.754, and 0.778, respectively. Molecular docking and 100-ns molecular dynamics simulations suggested interactions between 6PPDQ and candidate proteins, including CPT2, SHC2, SRMS, and STK35. Functional assays showed that 6PPDQ exposure altered proliferation, wound-closure capacity, and invasion in Caco-2 and HCT116 cells across the nanomolar concentration range, with non-monotonic and cell-line-dependent responses. In contrast, NCM460 cells showed no increase in EdU incorporation at 10 or 100 nM, whereas reduced proliferation at higher concentrations was accompanied by increased LDH release. 6PPDQ also altered the expression of several prognosis-associated candidate genes. These findings identify cellular phenotypes and candidate molecular responses associated with 6PPDQ exposure under the tested in vitro conditions, but do not establish their causal roles or in vivo relevance. Further mechanistic and in vivo studies are required.

Humans↗

PubMed2026

Engineering copper ferrite (CuFe2O4) nanocomposites for enhanced eco-friendly photocatalysis: a systematic critical review on mechanisms, performance, and environmental applications.

Water pollution caused by organic and inorganic contaminants, particularly dyes and pharmaceuticals, represents a major environmental challenge. Advanced oxidation processes based on photocatalysts have emerged as efficient and sustainable approaches for water and wastewater treatment. Copper ferrite (CuFe2O4) is considered a promising photocatalyst owing to its narrow bandgap, visible-light activity, chemical stability, and magnetic properties. Despite extensive experimental investigations, a comprehensive systematic comparison of CuFe2O4-based photocatalysts under diverse operational conditions has remained limited. In this study, a systematic review following PRISMA guidelines was conducted using studies published between January 2014 and November 2025 indexed in Scopus, PubMed, Web of Science, and ScienceDirect. From an initial pool of 397 studies, 98 articles met the inclusion criteria. Key parameters - including pollutant type, pH, catalyst dosage, initial pollutant concentration, irradiation time, light source, and degradation efficiency - were quantitatively compared to identify performance trends and operational optima. The results demonstrate that CuFe2O4-based nanocomposites, particularly heterojunction, Z-scheme, and S-scheme architectures combined with TiO2, g-C3N4, graphene, and metal oxides, achieve high degradation efficiencies (often >90 %) for a wide range of organic pollutants and selected inorganic contaminants (e.g., Cr(VI)). Enhanced charge separation and suppressed electron-hole recombination were identified as the primary factors contributing to improved photocatalytic activity. In addition, the intrinsic magnetic properties of these nanocomposites enable facile catalyst recovery and reuse. In conclusion, CuFe2O4-based nanocomposites, especially those incorporating advanced heterojunction architectures, emerge as highly efficient and magnetically recoverable photocatalytic platforms for sustainable water and wastewater treatment, with strong potential for scalable implementation and real-wastewater applications.

Catalysis↗

PubMed2026

Biomonitoring of industrial heavy metal pollution via enzymatic and metabolic responses in desert ants (Cataglyphis savignyi) and beetles (Tentyrum sp) as bioindicators.

The current work seeks to evaluate the effectiveness of Cataglyphis saviginyi and Tentyrum sp as indicators of pollution in the city's main industrial regions by analyzing their enzymatic activity and primary metabolites. Soil samples were collected at each site under investigation to analyze soil characteristics and heavy metal content. C. saviginyi and Tentyrum sp were collected across four consecutive seasons (2023-2024) to investigate enzymatic (GPT, GOT, ALP, ACP, LDH) and metabolic (lipid, protein, carbohydrate) biomarkers. The physicochemical properties of the soil differed substantially between the industrial areas and the control site. Soil heavy metal buildup was highest at industrial sites (1 and 4) compared to the control site, with the order being Zn > Cr > Cd > Cu. Heavy metal pollution indices were determined. Increased industrial activity from metal industries, ceramics, and chemical painting companies defines this area, as seen by the high Cdeg, mCd, PI, and PLI values derived for industrial sites 1 and 4. While C. saviginyi and Tentyrum sp deconcentrated and released Cr, Cd, and Zn into the soil via the biological accumulation factor (BAF), Cu acted as a macro-concentrator. Compared with the control site, industrial environments were shown to increase levels of GPT, GOT, LDH, ACP, protein, and carbohydrates in C. saviginyi. However, lipid and ALP activity was suppressed. at industrial sites, Tentyrum sp carbohydrate content was higher than at control sites, but GPT, GOT, ALP, ACP, LDH, protein, and lipid activities were all suppressed. Consequently, enzymatic and metabolic biomarkers proved to be sensitive indicators for assessing industrial heavy metal pollution in desert ecosystems.

Animals↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2026

Direct background subtraction LC-MS/MS assay for human plasma progesterone: Full validation and comparative application.

OBJECTIVE: To develop and validate a liquid chromatography-tandem mass spectrometry method based on direct background subtraction for the quantification of endogenous progesterone in human plasma. METHODS: Protein precipitation was used for sample preparation with deuterated progesterone as the internal standard. Chromatographic separation was performed on an ACQUITY C18 column using gradient elution with 0.1% formic acid in water and acetonitrile at a flow rate of 0.3 mL/min. Mass spectrometry was operated in positive electrospray ionization mode with multiple reaction monitoring. Instead of using analyte-stripped matrix or surrogate matrix, authentic plasma was directly used for all validation experiments. Quantitation was achieved by subtracting the background signal, and results were compared with those from the classical method using stripped matrix. RESULTS: Excellent linearity was achieved over 0.1-100 ng/mL (R2 ≥ 0.99). Precision, accuracy, recovery, matrix effect, and stability all met FDA and ICH M10 acceptance criteria. Compared with the classical method, the bias in Cmax and AUC0-t was within ±15%, indicating no significant difference between the two methods. CONCLUSION: The direct background subtraction method avoids laborious preparation of blank matrix, eliminates matrix effect discrepancies, and is simple, efficient, and low-cost. It can serve as a general strategy for endogenous substance determination.

Humans↗

PubMed2026

Inhibitory mechanism of phloretin on the AgrA LytTR domain-agr operon complex formation and its application in beef.

Staphylococcus aureus (S. aureus) represents a major foodborne pathogen whose enterotoxin production poses significant challenges to food safety due to its high environmental resistance and limited efficacy of conventional sterilization. Since the expression of enterotoxins is predominantly governed by the agr quorum sensing system, targeting this regulatory pathway has become a strategic choice for virulence control. This study elucidated the mechanism by which phloretin, a potential quorum sensing inhibitor, interferes with the agr system to attenuate virulence. To achieve this, the recombinant AgrA LytTR domain was expressed and purified, and its interaction with phloretin was characterized using thermal shift assays (TSA), electrophoretic mobility shift assays (EMSA), and molecular dynamics (MD) simulations. The results showed that phloretin specifically binds to the AgrA LytTR domain, enhancing its thermal stability and disrupting AgrA LytTR-agr operon binding by reducing the free energy of interaction between them, without causing significant structural rearrangement. Mechanistic analysis indicated that phloretin sterically hinders key β-sheet turn residues (HIS169, ASN201, ARG233), thereby impairing DNA recognition, downregulating RNAIII transcription, and inhibiting agr signaling. In cooked beef, phloretin significantly inhibited the secretion of enterotoxins and α-hemolysin, while delaying lipid oxidation and protein degradation, and maintaining the meat texture. These findings suggested that phloretin is a multifunctional substance with anti-virulence, antioxidant, and preservative properties, demonstrating its potential as a natural food preservative.

Phloretin↗