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PubMed2026

A chromosomal gtrB homolog and dam differentially contribute to dry-heat and high hydrostatic pressure resistance in Salmonella enterica.

Salmonella enterica can persist in low-moisture foods and shows enhanced dry-heat resistance under low water activity, posing significant food safety challenges. However, the genetic basis of extreme dry-heat resistance and its relationship with other processing stresses remain unclear. In this study, twelve S. enterica strains were screened for dry-heat treatment at 60 °C and 80 °C, with S. Infantis CICC21649 identified as the most resistant strain. Comparative genomics and transcriptional analysis identified candidate genes related to envelope integrity and regulation, including gtrB and dam. Deletion of the chromosomal gtrB homolog reduced dry-heat resistance, producing an additional 0.91-log10 reduction relative to the parent strain at 80 °C. Deletion of dam caused broader stress sensitivity, reducing resistance to both dry heat and high hydrostatic pressure, with the stronger phenotype observed under high hydrostatic pressure. Proteomic analysis of the chromosomal gtrB homolog mutant revealed broad alterations in envelope-associated proteins, transport functions, oxidative stress pathways, and central metabolism under dry-heat stress. These findings indicate that the chromosomal gtrB homolog is an important contributor to extreme dry-heat resistance, whereas dam contributes to resistance against both dry-heat and high hydrostatic pressure, likely through a broader regulatory role in stress adaptation. These results reveal distinct structural and regulatory layers underlying stress adaptation in S. enterica and provide practical guidance for low-moisture food processing by highlighting the need to account for strain-dependent and stress-specific resistance during process validation.

Hydrostatic Pressure↗
PubMed2026

Imaging‑based models for predicting cerebrovascular complications of carotid stenosis.

This is a protocol for a Cochrane review (prognosis). The objectives are as follows: Primary objective To systematically review and critically appraise multivariable prognostic models developed for adults (≥ 18 years) with carotid stenosis in which imaging biomarkers (e.g. plaque characteristics derived from magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) constitute the core predictors. The primary focus is to evaluate the predictive performance of these models for cerebrovascular complications - specifically ipsilateral ischaemic stroke and transient ischaemic attack (TIA) - which are the clinical outcomes to be predicted. Where feasible, we will summarise and compare the models' discrimination (C‑statistic/area under the curve (AUC)) and calibration (calibration‑in‑the‑large, calibration slope, observed‑to‑expected ratio) across studies, and assess their potential for clinical application and external validation. For the purpose of defining symptomatic carotid stenosis as an eligibility criterion and subgroup variable, we will include studies that also considered retinal ischaemia (e.g. retinal embolism, amaurosis fugax) as a qualifying event. Secondary objectives To describe the combinations of imaging markers, modelling techniques, sample sizes, and variable‑selection strategies used in the development of the included models To evaluate the performance of these models for additional secondary clinical outcomes: plaque progression or regression, incident high‑risk imaging features, and the transition from asymptomatic to symptomatic disease To explore whether predictive performance differs according to imaging modality (MRI versus CT versus contrast‑enhanced ultrasound (CEUS)) or technical protocol (e.g. 3 T versus 1.5 T, spectral CT versus conventional CT) For studies that report both cerebrovascular and broader cardiovascular outcomes (major adverse cardiovascular events, myocardial infarction, etc.), we will only extract the performance metrics relating to cerebrovascular events for the primary analysis. Performance metrics for cardiovascular outcomes will be considered exploratory and will not form part of the main synthesis.

Humans↗
PubMed2025

New insights into soil amendment: Impact of humic acid on typical antibiotic resistance in agricultural soil.

Humic acid (HA) addition can improve agricultural soil, but little is known about how it affects the soil resistome. In this study, we used selective agar plate combined with quantitative PCR (qPCR) and 16S rRNA gene sequencing to investigate how HA influences antibiotic resistant bacteria (ARB) and antibiotic resistant genes (ARGs) in soil contaminated with erythromycin and kanamycin. 0.1 % HA reduced the abundance of culturable erythromycin-resistant bacteria (ERB), while promoting the growth of kanamycin-resistant bacteria (KRB). Lysinibacillus and Paenibacillus were the dominant genera in ERB and KRB, respectively, governing the changes in their abundances. At this concentration, the Lysinibacillus abundance in ERB decreased from 96.74 % to 70.57 %. Meanwhile, that of Paenibacillus in KRB increased from 33.40 % to 77.44 %. The copy number of ermF decreased after HA addition, while that of ermB increased. Furthermore, 0.1 % HA significantly reduced the copy number and relative abundance of aadA1 and aac(6')-Ib (aka aacA4)-03 in the soil. Changes in these two types of ARB and ARGs were primarily driven by shifts in the microbial community structure. Soil physicochemical properties, particularly increased organic matter (OM), altered the absolute abundance of ermB. Meanwhile, changes in intI1 abundance determined the risk associated with aadA1 and aac(6')-Ib (aka aacA4)-03. These findings emphasize the dual role of HA in the dissemination of antibiotic resistance in agricultural soils and highlight the necessity of considering dose-dependent effects when applying HA as a soil amendment.

Soil Microbiology↗
PubMed2026

Genome-wide identification of the HSP70 superfamily in tropical sea cucumber Stichopus monotuberculatus and their expression analysis under low-salinity stress.

Heat shock proteins (HSPs) are a group of evolutionarily conserved molecular chaperones that serve as indispensable core regulators in preserving cellular homeostasis and orchestrating organismal stress responses. The tropical sea cucumber Stichopus monotuberculatus, a high-value aquaculture species, is sensitive to fluctuations in environmental salinity-a challenge that has emerged as a critical bottleneck limiting its large-scale commercial cultivation. However, no systematic investigation has been conducted to characterize the HSP70 superfamily in S. monotuberculatus and elucidate its functional roles in salinity adaptation. In the present study, we performed a comprehensive genome-wide scan and identified 19 HSP70 superfamily genes in the S. monotuberculatus genome, with the HSP70IV subfamily showing remarkable gene expansion, containing 8 distinct copies. Phylogenetic analysis, conserved motif identification, and gene structure characterization demonstrated high evolutionary conservation within each HSP subfamily. These genes were unevenly distributed across the chromosomes of S. monotuberculatus, and prediction of cis-acting elements revealed that their upstream regulatory regions were enriched with numerous functional elements associated with stress response and immune regulation. Salinity stress experiments revealed that under severe low-salinity conditions (18‰), the expression levels of SmHSPA14L and multiple HSP70IV subfamily members were significantly elevated, while SmHYOU1D was significantly downregulated; in contrast, only subtle changes were detected in the expression of most HSP70 genes under moderate low-salinity stress (24‰). These findings strongly suggest that HSP70 genes, particularly the expanded HSP70IV subfamily, may act as key modulators in the low-salinity stress response. This work provides valuable insight into the molecular mechanisms underlying salinity adaptation in tropical sea cucumbers.

Animals↗
PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗
PubMed2026

The composition of the periostracum in the razor clam Sinonovacula constricta and the mantle's response to sulfide.

The razor clam Sinonovacula constricta inhabits sulfide-rich intertidal sediments and exhibits remarkable tolerance to this toxicant, yet the role of its periostracum in sulfide adaptation remains poorly understood. In this study, we investigated the composition and structure of the periostracum proteins, and the response of the mantle to sulfide stress. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed that the periostracum is approximately 10 μm thick and contains 1.43 wt% sulfur, and proteomic analysis further confirmed the presence of organic sulfur (Cys/Met-rich proteins), suggesting its involvement in sulfur deposition. Using LC-MS/MS, we identified 77 high-confidence proteins from the periostracum, which were classified into six functional categories: enzymes, framework proteins, immune-related proteins, calcium ion-related proteins, other proteins, and proteins with unknown functions. Phylogenetic analyses of representative proteins revealed bivalve-specific evolutionary patterns, with several proteins exclusively present in Bivalvia, such as Unknown protein 2 and 7, which possess signal peptides and low-complexity domains. For the sulfide exposure experiment, razor clams were subjected to three Na2S concentrations (0, 10, and 100 μM). qPCR analysis showed that, compared with the control group, Chitin-binding protein 3 and Tyrosinase were significantly upregulated in the mantle, peaking in the 100 μM group at 48 h (5677.84-fold and 157.20-fold, respectively), whereas Collagen and Cadherin 3 were generally suppressed. This study represents one of the most comprehensive proteomic profiles of the razor clam periostracum and highlights the mantle's potential role in sulfide tolerance, offering insights for sulfur-tolerant aquaculture breeding and bioremediation applications.

Animals↗
PubMed2026

Comparative transcriptome analysis reveals ncRNA-mediated regulatory networks associated with muscle crispiness in grass carp.

Non-coding RNAs (ncRNAs) have been demonstrated to be involved in muscle development and to function as key regulators. However, the molecular mechanism underlying muscle crispiness in grass carp (GC) remains poorly understood, and whether these ncRNAs are involved in its regulation is still unknown. In the current investigation, differentially expressed (DE) RNAs (including lncRNAs, circRNAs, miRNAs, and mRNAs) were identified; concomitantly, target genes prediction was conducted, and functional and signaling pathway enrichment analyses were performed. Pathways related to muscle crispiness were identified, and the competitive endogenous RNA (ceRNA) (lncRNA/circRNA-miRNA-mRNA) regulatory network was further constructed. The results showed that a total of 126 DE-lncRNAs, 17 DE-circRNAs, 329 DE-miRNAs, and 442 DE-mRNAs were identified in muscle tissues of both the GC and crisp grass carp (CGC). GO and KEGG enrichment analyses revealed that target genes of DE-ncRNAs were significantly enriched in signaling pathways, including structural constituents of muscle, apoptosis, oxidative phosphorylation, and regulation of actin cytoskeleton, suggesting that these pathways may be involved in muscle texture remodeling. Subsequently, DE-RNAs enriched in related pathways were identified, and a core ceRNA regulation network comprising 3 lncRNAs, 4 circRNAs, 3 miRNAs, and 17 mRNAs was constructed. Additionally, 10 DE-RNAs from randomly selected groups were validated by qRT-PCR. Our findings not only provide scientific evidence elucidating the molecular mechanisms underlying muscle crispiness in GC but also establish a foundation for studying changes in muscle textural qualities across other fish species.

Animals↗
PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗
PubMed2026

Redefining the real problem in psychedelic trials: Why fighting the Lessebo matters more than blinding integrity.

Imperfect blinding is not specific to psychedelic trials. In randomized trials, treatment allocation is frequently correctly guessed, yet blinding integrity is rarely assessed outside of psychedelic research and is generally not considered a barrier in regulatory evaluation. The intense debate in psychedelics may reflect a broader double standard affecting mental health research, when uncertainties arising from imperfect blinding are confounded by those linked to patient-reported outcome measures. Indeed, people living with mental disorders are often viewed as unreliable reporters, despite well-documented limitations of clinician-rated scales and the absence of robust biological markers of symptomatic change. Importantly, it is the maintenance of reasonable doubt of treatment allocation that sustains internal validity and ethical feasibility of placebo-controlled designs, rather than perfect blinding. Concerns about expectancy bias in psychedelic trials are closely tied to blinding debates. When allocation is inferred, expectations may cluster in the arm perceived as active or in stereotyped experiences and influence outcomes differently in active and control arms, leading to a risk of lessebo, a negative placebo effect due to the negative expectation related to receiving a placebo. However, we argue that an underrecognized mechanism of lessebo is disappointment. This risk may reflect insufficient clinical management of disappointment rather than pre-treatment expectation alone. We therefore propose shifting the emphasis from preserving inevitably imperfect blinding towards mitigating disappointment in both arms. Establishing non-stereotyped expectations prior to treatment through structured psychoeducation, strengthened therapeutic alliance, and realistic preparation would help avoid lessebo effects. Such strategies would enhance ethical rigor, interpretability, and the clinical usefulness of psychedelic trials.

Humans↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed2026

Effects of rumen fluid transplantation on longissimus dorsi muscle development in Xizang sheep: An association analysis based on transcriptomic and serum metabolomic profiles.

This study aimed to investigate the effects of rumen fluid transplantation (RFT) on the growth and development of the longissimus dorsi muscle in female Xizang sheep. After RFT, muscle lightness differed significantly between the two groups, with the LDC group showing significantly higher lightness than the LDT group. In contrast, no significant differences were observed between groups in other muscle phenotypic traits, including drip loss, pH, cooking loss, shear force, redness, and yellowness. Antioxidant-related indices (SOD, GSH-PX, MDA, CAT, and T-AOC) also showed no significant differences between groups. Histological analysis revealed that muscle fiber length, width, and density were significantly greater in the experimental group than in the control group. Transcriptomic analysis identified 515 differentially expressed genes (DEGs), of which 419 were downregulated. KEGG analysis indicated that genes involved in muscle development-related pathways, such as cell adhesion and the PI3K-Akt signaling pathway, were predominantly downregulated. Key serum metabolites (L-kynurenine, IPA, allantoin, and propionylcarnitine) showed highly significant positive correlations with muscle fiber growth indices. In contrast, metabolites such as l-carnitine, acetylcarnitine, and citrulline were negatively correlated with muscle fiber growth, but positively correlated with the expression of muscle structure-related genes (COL11A1 and EFNA5) and with meat lightness. Overall, this study provides new insights into the potential molecular basis by which RFT influences muscle growth and development. However, the mechanisms by which RFT affects muscle development and meat quality-related traits remain unclear and warrant further investigation.

Animals↗
PubMed2026

A multi-model genome-wide association study identifies genetic variants underlying resistance to Largemouth Bass Ranavirus (LMBV) in Micropterus salmoides.

Largemouth bass (Micropterus salmoides) is an economically important freshwater aquaculture species, yet recurrent outbreaks of Largemouth Bass Ranavirus (LMBV) continue to impair production and cause substantial losses. The genetic basis of host variation in LMBV resistance remains insufficiently characterized. Here, we applied a multi-model genome-wide association study (GWAS) to identify loci associated with resistance following a controlled challenge with the LMBV-23PY strain. Whole-genome resequencing was performed for 146 phenotyped fish, including 72 susceptible and 74 resistant individuals. After stringent quality control, 877,262 high-quality variants were retained and tested using six GWAS models. Across binary survival status and survival time phenotypes, 32 shared suggestive variants were consistently detected across models, representing suggestive loci for LMBV-23PY resistance. Genes within ±50 kb of these loci were annotated, and functional enrichment highlighted immune- and redox-related biological processes. Three prioritized candidates-GSTT3L (glutathione S-transferase theta-3-like), CGRP2 (calcitonin gene-related peptide 2), and NPPC (natriuretic peptide C)-were associated with pathways involved in oxidative stress responses and immune regulation. Collectively, these results provide insight into the genetic architecture of LMBV-23PY resistance in largemouth bass and identify suggestive variants and associated candidate genes for downstream validation, functional interrogation, and the development of marker-assisted and genome-enabled breeding strategies.

Animals↗
PubMed2026

Evolutionary expansion of the NF-Y gene family in bivalves and divergent subunit responses to thermal and pathogenic stress in the noble scallop.

Nuclear factor Y (NF-Y) is a conserved eukaryotic transcription factor complex that specifically interacts with the CCAAT motif. Prior research has demonstrated that this gene family participates in various biological processes, encompassing growth, development, and stress responses, across a broad spectrum of organisms. However, research on the role of the NF-Y family in bivalves remains limited. In this study, we comprehensively identified the NF-Y family in 34 bivalve species, and further investigated its expression in the noble scallop Chlamys nobilis. A total of 296 NF-Y genes were identified and classified into three subfamilies, NF-YA, NF-YB, and NF-YC. Phylogenetic analysis revealed that NF-YA and NF-YC have remained relatively conserved, whereas NF-YB has undergone significant expansion. Additionally, while substantial disparities in gene copy numbers exist across species, the motif composition and exon-intron structures within each subfamily demonstrate notable conservation. Tissue expression profiling revealed distinct expression patterns among CnNF-Y genes, with several members exhibiting relatively high transcript abundance in gonadal tissues. Furthermore, qRT-PCR results demonstrated that CnNF-YA2, CnNF-YB6, and CnNF-YC were significantly and continuously upregulated under heat stress. Conversely, several genes, particularly CnNF-YA2, CnNF-YB3, and CnNF-YB4, exhibited dynamic transcriptional responses to Vibrio parahaemolyticus exposure. These findings enhance our understanding of the evolutionary trajectory and functional diversification of the NF-Y gene family in bivalves, laying a theoretical foundation for future research on thermal adaptation, immune regulation, and molecular breeding in scallops.

Animals↗
PubMed2026

Mechanisms of high-humidity hot air impingement blanching (HHAIB) on microbial counts, functional properties, phenolic profile transformation, and volatile compounds in celery stalks (Apium graveolens L.).

In this study, celery stalks were pretreated with different durations (0-150 s) of high-humidity hot air impingement blanching (HHAIB), followed by far-infrared radiation assisted pulsed vacuum freeze-drying (FIR-PVFD) at 60, 65, and 70 °C. The effects of HHAIB on the physicochemical properties, composition and transformation of phenolic compounds, volatile components, and antioxidant capacity of FIR-PVFD-dried celery stalks were systematically investigated. The results showed that HHAIB not only effectively reduced the counts of total mesophilic aerobic bacteria (TMAB) and total yeast and mold (TYM), but also decreased the relative activities of polyphenol oxidase (PPO) and lipoxygenase (LOX) by more than 91% after 90 s of treatment. HHAIB altered the cellular structure of celery stalks, shortened the drying time by 29.33-41.43%, and improved their hydration properties. HHAIB pretreatment promoted the conversion of bound phenolics to free phenolics in celery stalks, with significant increases in the contents of p-coumaric acid, apigenin, graveobioside A, and other components. The total free phenolic content increased by 56.99%, thus HHAIB enhanced the antioxidant activity. An electronic nose and sensory evaluation revealed that HHAIB-pretreated celery stalks better retained the characteristic herbal and pungent notes. GC-MS results indicated that HHAIB treatment optimized the aroma profile by regulating the contents and composition of terpenes, aldehydes, ketones, alcohols, and aromatic compounds.

Apium↗
PubMed2026

Understanding Suicide through Coroners' Narratives: implications for primary care from a mixed‑methods study of 157 Coroners' reports.

Suicide is a major public health concern, and general practice is often a recent point of contact before death. While mental illness is well recognised, the broader social and contextual factors influencing suicide risk remain under-reported in primary care and epidemiological research Aim To describe the demographic, clinical, and psychosocial characteristics of individuals who died by suicide, integrating coronial quantitative data with qualitative narrative accounts to identify implications for primary/ secondary care and public health. Design and setting Explanatory sequential mixed‑methods study of 157 consecutive deaths by suicide recorded by coroners (2018-19) across five English local authorities. Method Demographic, clinical, and social data were extracted from coroners' records and summarised descriptively. Narrative case summaries were coded and analysed thematically to identify contextual, relational, and service factors preceding death. Results Of 157 individuals: 79% were male; 65% lived in the most deprived IMD quintile; 85% had a diagnosed mental health condition; 62% had a long‑term physical illness; 41% had a previous suicide attempt. About half consulted a GP in the preceding three months; mental health featured in about half of those consultations. Common stressors were relationship breakdown (37.2%), housing instability (22.1%), and work pressures (18.2%). Seven interlinked themes were identified: Mental health; Alcohol/Substance use, Physical health; Social connectedness; Life course trauma, Socioeconomic and Structural Vulnerability; Healthcare access. Service transitions were key vulnerability points Conclusion Coroners' records offer important insights into the complex circumstances preceding suicide and highlight opportunities for GPs to recognise intersectional complexity and support integrated, cross-sector suicide prevention approaches.

General Practice↗
PubMed2026

Depth-dependent microbial succession and interspecies hydrogen transfer drive pit mud maturation in Chinese strong-flavor baijiu fermentation.

Microbial communities in fermentation pit mud play a key role in determining the quality of Chinese strong-flavor baijiu (CSFB). However, the ecological processes underlying pit mud maturation across spatial and temporal scales remain unclear. In this study, amplicon sequencing and metagenomic analyses were employed to investigate the taxonomic succession, community assembly, and metabolic functions of bacterial and archaeal communities during the transition from fresh pit mud (FPM) to new pit mud (NPM) and old pit mud (OPM). A pronounced depth-dependent succession pattern was observed, with 4 cm representing a critical ecological boundary separating distinct community structures and maturation trajectories. During surface-layer maturation, community assembly shifted from stochastic to deterministic processes, accompanied by homogeneous selection and increasing network complexity. In contrast, stochastic processes remained dominant throughout deep-layer maturation. Metagenomic analyses revealed a functional transition from lactate and acetate production, primarily associated with Lactobacillus in FPM and NPM, to butyrate and caproate production associated with Clostridium and Caproiciproducens in OPM. This functional transition was accompanied by enhanced amino acid metabolism, which was associated with the enrichment of Proteiniphilum and Aminobacterium. Notably, methanogen-mediated interspecies hydrogen transfer (IHT) emerged as a key ecological feature during pit mud maturation. In OPM, IHT networks primarily involving Methanobacterium and Methanosarcina linked methanogenesis with reverse β-oxidation through diverse hydrogen-transfer pathways, reinforcing metabolic interactions underlying caproate production. These findings provide new insights into the ecological mechanisms underlying pit mud maturation and offer a theoretical basis for the directed cultivation of high-quality pit mud in CSFB production.

Hydrogen↗
PubMed2026

Transformation of antibiotics mediated by iron-bearing minerals: A review.

Iron-bearing minerals are ubiquitous in water, sediments and soil, where their surface chemical properties and redox activity can play an important role in degradation of trace antibiotics. This review systematically summarizes the roles of various iron-bearing minerals in chemical transformation and microbial degradation of antibiotics and reaction mechanisms involved, and refines the critical idea for iron-driven control of antibiotics with trace level in natural environment. Overall, antibiotics removal in the presence of iron-bearing minerals involves combination of adsorption, surface oxidative degradation, photo-induced degradation, Fenton-like reaction and microbial degradation. Adsorption of antibiotics by Fe(III)-minerals involves electrostatic interaction, complexation, H-bonding, π-π interaction and hydrophobic interaction. Adsorbed antibiotics form complexes with Fe(III)-minerals, undergoing electron transfer to generate radical intermediates, subsequently generating final products through hydroxylation, dealkylation, and deamination. Additionally, Fe(III)-minerals can be excited to produce electrons and holes under sunlight and to produce antibiotics-degrading hydroxyl radical through O2 reduction, H2O oxidation and ligand-to-metal charge transfer. Reduced iron minerals can activate oxygen to participate in Fenton-like degradation reactions. Finally, antibiotics are mainly removed by bio-driven Fenton reaction and direct enzyme biodegradation. The presence of iron-bearing minerals can promote antibiotics microbial degradation by providing nutrients for microorganisms or by changing microbial activity and microbial community structure. Existing problems and future research directions are identified. New insights for application of iron-bearing minerals in transformation of antibiotics are proposed. The work aims to suggest new methods and insights for pollution control and remediation of emerging contaminants including trace antibiotics in the natural environment.

Anti-Bacterial Agents↗