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PubMed2026

Three-dimensional source apportionment and quantitative characterization of horizontal and vertical transport fluxes of O3 and its precursors in the Beijing-Tianjin-Hebei region, China.

Persistent surface ozone (O3) pollution in the Beijing-Tianjin-Hebei (BTH) region is driven by coupled precursor emissions and multi-scale transport, yet its altitude-dependent transport and source contributions remain insufficiently quantified. Here we integrated the Weather Research and Forecasting and the Comprehensive Air Quality Model with Extensions with the Ozone Source Apportionment Technology and a quantitative transport-flux framework to characterize three-dimensional source apportionment and horizontal/vertical fluxes of O3, Volatile Organic Compounds‌ (VOCs), and Nitrogen Oxides (NOx) across dynamic meteorological scenarios. Simulations showed that VOCs and NOx were dominated by local emissions near the surface (73.61 %-82.18 %), whereas surface O3 was primarily controlled by regional transport, with local contributions of only 11.01 %-13.75 %. Notably, the transport dominance further strengthened with altitude, exceeding 93 % at 1.8 km. Industrial and transportation emissions together contributed more than 75 % of precursor emissions and account for approximately 80 % of O3 formation, while favorable/unfavorable meteorological years modulated long-range transport efficiency and the vertical distribution of contributions. Horizontal flux analysis highlighted three major pathways (Northwest-Southeast, Southeast-Northwest, and Southwest-Northeast), with Shijiazhuang serving as a critical pollutant "sink" across altitude layers. Vertical fluxes revealed an altitude transition near 600 m: net downward transport dominated below 600 m, whereas enhanced summer convection promoted upward transport above 600 m. These results support altitude-dependent, scenario-specific strategies for coordinated regional O3 mitigation in the BTH region.

Ozone↗

PubMed2026

Integrated widely targeted metabolomics and GC-IMS reveal dynamic flavor, nutritional, functional, and metabolic profiles in macadamia kernels during processing.

Different processing stages influence the color, flavor, and antioxidant activities of macadamia kernels. However, the biochemical mechanisms that occur during processing are not well known. This study integrated widely targeted metabolomics (UPLC-MS/MS) with GC-IMS to systematically characterize non-volatile and volatile compounds in macadamia kernels across key three sample groups: fresh kernels (FMN), low-temperature-dried kernels (DMN), and roasted kernels (BMN). A total of 622 non-volatile metabolites and 52 volatile compounds were identified. Low-temperature drying promoted the accumulation of phenolic acids and flavonoids, enhancing antioxidant capacity. Roasting degraded heat-sensitive nutrients but generated flavor compounds via Maillard reaction and lipid oxidation, shifting aroma from green to nutty notes. Nutritional assessment confirmed that roasting significantly reduced antioxidant activities and bile acid binding capacity. Pearson correlation analysis verified the key metabolite-antioxidant relationships. These findings provide critical insights into metabolic dynamics during nut processing and establish a scientific basis for optimizing thermal processing strategies.

Metabolomics↗

PubMed2026

Enhancement flavor quality in Zhao'an Baxian oolong tea through enhanced turning-over process.

A systematical investigation on the effects of turning-over intensity on the flavor formation of Zhao'an Baxian oolong tea (ZBT) was performed, through a comparative analysis of heavy turning-over (HT) and light turning-over (LT) treatments in this study. The tea samples were subjected to proteomic and metabolomic analyses, combined with quantitative descriptive analysis (QDA) and electronic sensory (E-tongue/E-nose) evaluation. The results demonstrate that HT significantly reduced the content of bitter and astringent compounds, such as catechins and flavonol glycosides, while promoting the accumulation of umami-related amino acids. Concurrently, HT enhanced the biosynthesis of key floral and fruity volatiles, such as β-ocimene, geraniol, benzaldehyde, jasmone by activating stress-responsive metabolic pathways. These coordinated biochemical changes, driven by enzyme-catalyzed reactions in response to prolonged mechanical wounding and environmental stress, collectively improved the overall sensory profile of ZBT. These findings provide a mechanistic foundation for improving ZBT production, with clear implications for quality control and flavor-oriented product development.

Tea↗

PubMed2026

Integrated assessment of biocontrol potential and genome analysis of endophytic Bacillus velezensis MGL-B1 against mango stem-end rot.

Mango stem-end rot is a globally significant postharvest disease that severely threatens the mango industry, primarily caused by Botryosphaeria dothidea. However, information on biocontrol agents targeting this pathogen in mango remains limited. In this study, we isolated and identified a strain of Bacillus velezensis MGL-B1 from mango leaf tissues for the first time, which exhibited broad-spectrum antifungal activity. Both in vitro and in vivo assays demonstrated that MGL-B1 effectively inhibited the growth of B. dothidea, with an in vivo biocontrol efficacy reaching 83.72 ± 5.10%, comparable to that of the commonly used chemical fungicide thiabendazole. Further mechanistic analysis revealed that MGL-B1 acts by directly disrupting the integrity of the pathogen's mycelial cell membrane. In addition, its released volatile organic compounds (VOCs) also displayed significant antifungal activity, with components such as 2-nonanone, 2-nonanol, and phenylethyl alcohol being confirmed to exert antifungal effects in in vitro fumigation assays. qPCR analysis showed that MGL-B1 treatment significantly upregulated the transcriptional levels of genes involved in plant-pathogen interaction, phenylpropanoid biosynthesis, and antioxidant defense pathways in mango fruits, with upregulation folds of 16.32, 37.19, and 75.93, respectively; meanwhile, the expression of browning-related genes such as polyphenol oxidase (PPO) was markedly suppressed. Whole-genome sequencing further revealed 14 biosynthetic gene clusters for antimicrobial compounds, including five unknown gene clusters. Collectively, B. velezensis MGL-B1 represents a promising biocandidate strain with multiple antifungal mechanisms and excellent control efficacy, providing a valuable resource for green and sustainable management of mango diseases.

Mangifera↗

PubMed2026

CoLchicine for Treatment of OsteoArthritis of the Knee (CLOAK): Clinical and biochemical outcomes from a three-month double-blind, placebo-controlled study.

OBJECTIVE: Knee osteoarthritis (KOA) causes pain and progressive disability, but pharmacologic treatments are limited. Colchicine inhibits inflammation that might modulate KOA, but efficacy trials have yielded mixed results. We tested whether colchicine, without concurrent NSAIDs, improved KOA pain, function, synovial effusion size, and OA-associated inflammatory serum biomarkers. METHODS: Participants with symptomatic KOA and radiographic Kellgren-Lawrence grades 2/3 were randomized to receive three months of daily colchicine or placebo in a double-blind manner, with no concurrent NSAID use. The primary outcome was between-group change in visual analog score (VAS) for index knee pain. Secondary outcomes included changes in Knee Osteoarthritis Outcome Scores (KOOS), size (depth in millimeters) of sonographically-identified effusions, acetaminophen use, and changes in OA-related serum biomarkers. RESULTS: From baseline to end of study of 120 enrolled participants, no significant differences were observed in improvement of VAS pain, KOOS scores or effusion size. Subsets of participants with more severe VAS pain, worse radiographic disease, or higher hsCRP or serum urate levels at baseline also showed no significant clinical benefit from colchicine compared to placebo. In contrast to the clinical outcomes, colchicine treatment was associated with significant or trending improvement in multiple OA-related serum biomarkers including hsCRP and β-NGF (p < 0.05) and PGE2, IL-1ra, IL-8, and VEGF (p < 0.16). CONCLUSION: This double-blind placebo-controlled trial of colchicine for KOA failed to demonstrate improvement in pain, function, or synovial effusion size in comparison to placebo at three months. Early improvement in OA-associated inflammatory biomarkers suggests a possible longer-term clinical benefit. Clinical Trials Registration No NCT03913442.

Humans↗

PubMed2026

Design of an innovative framework based hybrid catalyst for simultaneous and sensitive monitoring of food additive and preservative of vanillin and nitrite in direct samples.

As vanillin (VAN) and nitrite (NIT) contamination in the food chain poses substantial threats to environmental and public health, rapid and portable detection is essential. The present study presents the first electrochemical sensor report based on a hybrid composite of Ni-TPA-MOF and MoS2/Co3O4. The oxidation of VAN and NIT exhibited sharp peaks and less over-potential on Ni-TPA-MOF/MoS2/Co3O4/GCE than on control electrode surfaces. On modified composite electrode surfaces, pH and scan rate were investigated for VAN and NIT. Further, the oxidation current exhibited high linearity at VAN and NIT concentrations of 5 nM-1000 μM and 3 nM-1250 μM, with detection limits of 0.102 nM and 0.073 nM (S/N = 3). We also applied anti-interfering ability (five/ten-fold excess of co-interfering compounds) and practical tests to various food-based real samples, with high recoveries of 98.85-102.41%. This study highlights the catalytic properties of Ni-TPA-MOF/MoS2/Co3O4 and demonstrates the sensor as a promising tool for food safety.

Benzaldehydes↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

The effect of dexmedetomidine in mechanically ventilated patients with sepsis and septic shock: a meta-analysis of randomized controlled trials.

PURPOSE: Dexmedetomidine (DEX) is a central sympatholytic with sedative properties widely used in critically ill patients. However, its effects in patients with sepsis and septic shock remain controversial. This meta-analysis evaluated the efficacy and safety of DEX compared to other sedatives in mechanically ventilated patients with sepsis and septic shock. METHODS: A systematic search was conducted across PubMed, Embase, Scopus, and Cochrane Library from inception through May 1, 2025 for randomized controlled trials comparing DEX with other sedatives or placebo in mechanically ventilated patients with sepsis and septic shock. Primary outcomes included overall mortality and Sequential Organ Failure Assessment (SOFA) scores. Secondary outcomes encompassed duration of mechanical ventilation (MV), length of stay in Intensive Care Unit (ICU), incidence of hypotension and bradycardia. RESULTS: Fifteen studies involving 3,882 patients (1,945 in the DEX group, 1,937 in the control group) were included. DEX was demonstrated no significant differences compared to other sedatives or placebo in overall mortality (Risk Ratio [RR] 0.98, 95% Confidence Interval [CI] 0.90 to 1.07, p = 0.71, I2 = 0%), SOFA scores (Mean Difference [MD] - 0.14, 95% CI -0.81 to 0.52, p = 0.67, I2 = 0%), length of stay in ICU (MD -0.32, 95% CI -1.69 to 1.06, p = 0.65, I2 = 77%), or incidence of hypotension (RR 1.15, 95% CI 0.81 to 1.62, p = 0.44, I2 = 14%). However, DEX significantly reduced the duration of MV (MD -0.54, 95% CI -0.98 to -0.10, p = 0.02, I2 = 25%) but was associated with an increased incidence of bradycardia (RR 1.67, 95% CI 1.22 to 2.28, p = 0.001, I2 = 0%). CONCLUSIONS: In mechanically ventilated patients with sepsis and septic shock, DEX shortened duration of MV but was associated increased bradycardia risk. No mortality or organ dysfunction benefits were observed. These findings suggest DEX is a reasonable therapeutic option to facilitate earlier ventilator weaning in selected patients (particularly those without shock), but careful monitoring for cardiovascular adverse effects is warranted.

Humans↗

PubMed2026

Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.

This study focused on L.brevis LD1.0600 with excellent fermentation traits: it analyzed genome-wide key regulatory genes for micro-metabolites, combined with fermented cherry juice flavor metabolomics data, and used machine learning to explore correlations between gene regulation, metabolite production, and flavor formation. The SVM model screened and verified fermented cherry juice VOCs; through OAV and flavor wheel analysis, LD1.0600 emerged as the top-performing strain, with a sweet, fruity dominant aroma. Key aroma-active components (OAV > 100) included 2-methoxy-4-vinylphenol, benzaldehyde, 2-methyl-butanoic acid and hexanoic acid, and 2-methoxy-4-vinylphenol and hexanoic acid elevated by LD1.0600-regulated genes (Chrom1-001884, Chrom1-000925, fabF and Chrom1-000199). At the same time, through research, a "strain screening-SVM screening of DVCs-OAV screening of key aroma components-whole genome sequencing of flavor regulatory genes" system was established. This system can not only be applied to the screen fermentation strains, but also can be extended to the application of other fermentation products.

Fermentation↗

PubMed2026

Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical↗

PubMed2026

Mechanistic insights into flavor deterioration in bitter sturgeon caviar: Evidence from lipidomics and metagenomics.

This study systematically compared the flavor and multi-omics differences between normal caviar and bitter caviar based on quantitative descriptive analysis (QDA), volatile compounds (VOCs) analysis, untargeted lipidomics, and metagenomics. The results showed that bitter caviar was characterized not only by increased bitterness, but also by decreased positive sensory attributes, including buttery, nutty, and marine fresh. VOCs analysis indicated that the volatile profile of bitter caviar was reorganized. Compounds such as 3-hydroxy-2-butanone, 1-octen-3-ol, and (E, Z)-2,6-nonadienal showed higher relative odor activity values (rOAVs); however, these changes did not improve its overall sensory experience. Untargeted lipidomics identified 492 differential lipids. These changes were mainly characterized by decreased PC and increased DG and LPC in bitter caviar. KEGG pathways analysis showed that these differential lipids were mainly associated with glycerophospholipid metabolism, choline metabolism in cancer, and retrograde endocannabinoid signaling. Metagenomic analysis showed that bacteria dominated the microbial community of caviar. Among them, Bacillus and Micromonospora showed relatively high abundance in the caviar microbiota. They were also closely associated with lipid metabolic changes involving PC, DG, and LPC, suggesting their potential as candidate targets for future microbiota-directed regulation of caviar quality. These findings provide new insights into the mechanisms underlying sensory deterioration and flavor formation in bitter caviar, and offer a theoretical basis for improving caviar quality in industrial production.

Animals↗

PubMed2026

Genome-wide characterization of the TGF-β superfamily identifies bmp15, gdf9, and gsdf as sex-biased candidate regulators of gonadal differentiation in the synchronous hermaphrodite Plectropomus leopardus.

The transforming growth factor-β (TGF-β) superfamily plays conserved roles in vertebrate reproduction and gonadal sex differentiation. However, its genomic repertoire and sex-biased expression patterns remain unclear in the leopard coral grouper (Plectropomus leopardus), a species with synchronous hermaphroditism. Here, we performed a genome-wide identification of the TGF-β superfamily, identifying 42 genes from the chromosome-level genome. Phylogenetic and synteny analyses indicated that segmental duplication under purifying selection contributed to family expansion. Expression profiling across multiple tissues and four gonadal developmental stages (undifferentiated, 120 dph; early differentiated, 15 months; mature testis, 3 years; mature ovary, 3 years) identified eight gonad-enriched genes, among which bmp15 and gdf9 exhibited pronounced female-biased expression, with transcripts localized exclusively to the oocyte cytoplasm, particularly in stage II-III oocytes. In contrast, gsdf showed male-biased expression and was localized in spermatogenic cells of the testis. These reciprocal expression patterns indicate that bmp15/gdf9 and gsdf are candidate factors associated with gonadal sex differentiation. Our study provides the first comprehensive characterization of the TGF-β superfamily in P. leopardus and highlights bmp15, gdf9, and gsdf as candidate sex-differentiation factors in this hermaphroditic species.

Animals↗

PubMed2026

Factors influencing the enhancement of the new iron triangle in healthcare organisations.

PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise´s Multiplication Appliqué´ a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.

Humans↗

PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Role of External Vibratory Lithoclast (Lithecbole) in Improving Lower Pole Stone Clearance After Extracorporeal Shock Wave Lithotripsy (ESWL).

Background and Objective: Extracorporeal shock wave lithotripsy (ESWL) is a widely used non-invasive treatment for renal stones. However, its effectiveness in clearing stones located in the lower pole of the kidney is often limited due to the anatomical challenges that impede the spontaneous passage of fragmented stones. This study evaluate the efficacy and safety of External Physical Vibration Lithecbole (EPVL) when used as an adjunctive therapy following ESWL in patients with lower pole renal stones, with a focus on improving stone clearance rates. Materials and Methods: This prospective interventional study was conducted at Al Yarmouk Teaching Hospital over two years and included 100 patients with 10-15 mm lower pole renal stones. Patients were randomized into two groups: The ESWL alone and ESWL plus EPVL. All patients received two ESWL sessions (3000 shocks/session). The treatment group additionally received EPVL therapy using a flank-applied vibration device. Follow-up was performed using ultrasound and KUB radiography. Statistical analysis was conducted using appropriate tests with significance set at p<0.05. Results: Baseline characteristics, including age, gender, weight, stone size and location, were statistically comparable between groups. The mean stone size was 12.4±1.7 mm. A significantly higher stone-free rate was observed in the ESWL+EPVL group compared to the ESWL-only group (66% vs. 48%, p = 0.027). Although the residual stone size showed a numerical difference favoring EPVL, it was not statistically significant (p = 0.11). Complication rates were low, mild and similar in both groups (p = 0.3). Logistic regression analysis revealed that smaller stone size (p<0.001) and lower patient weight (p = 0.030) were significantly associated with successful stone clearance. Subgroup analysis further confirmed that patients with stones <11 mm or a weight <70 kg had the highest stone-free rates. Conclusion: In this initial evaluation, EPVL demonstrated a safe and effective adjunct to ESWL in enhancing stone clearance for lower-pole renal stones. Its application was associated with improved treatment outcomes without increasing complication rates. Further studies with extended EPVL sessions and longer follow-up are warranted.

Humans↗

PubMed2026

Comparative profiling of microbial community structure, enzyme potential, metabolic features, and volatile composition in craft and Jiafan Huangjiu processes.

Craft Huangjiu and Jiafan Huangjiu represent two distinct industrial Huangjiu product outcomes with contrasting volatile profiles. This study compared craft Huangjiu (L70) and Jiafan Huangjiu (L79) to characterize their physicochemical, microbial, gene-level functional, metabolic, and volatile features. Because L70 involved mid-fermentation addition of finished Huangjiu, this comparison was not intended to isolate the sole effect of fermentation interruption versus continued fermentation. L79 showed more extensive carbon and nitrogen utilization, with lower residual substrates and higher ethanol and acetic acid contents than L70, whereas L70 retained a less complete fermentation state. At the volatile level, GC-MS and volatile metabolomics consistently showed an ester-enriched profile in L79 and a more alcohol-dominant profile in L70. FlavorDB-based putative annotation and threshold-based OAV analysis further indicated distinct database-assigned descriptor distributions and potential odor-active compounds, with more OAV > 1 ester-related compounds in L79. Metagenomic analysis showed that L70 was dominated by Lactobacillus acetotolerans, whereas L79 contained higher relative abundances of Saccharomyces cerevisiae, Aspergillus oryzae, Aspergillus flavus, and Fructilactobacillus fructivorans. Metagenomic functional annotation showed higher representation of hydrolysis-related CAZy genes and ester-related enzyme annotations in L79. KEGG-based pathway mapping further indicated greater gene-level potential for ethanol-, acetate-, and acetyl-CoA-related metabolism in L79. Accordingly, the L70 profile should be interpreted as the integrated final-product outcome of process intervention, exogenous input, and subsequent fermentation. The findings provide a comparative basis for future flavor regulation and process optimization in Huangjiu and other fermented alcoholic beverages.

Volatile Organic Compounds↗

PubMed2026

Integrated electronic nose, GC-MS, and metagenomic analyses reveal volatile flavor and microbial community differences in heap-fermented grains of Jiangxiangxing Baijiu across different fermentation degrees.

The fermentation degree of heap-fermented grains in Jiangxiangxing Baijiu production is a critical factor influencing base Baijiu quality. However, conventional assessment methods largely rely on empirical experience and therefore suffer from limited objectivity and accuracy. In this study, integrated volatile profiling and metagenomic approaches were employed to investigate volatile characteristics and microbial functional potential differentiation in fermented grains with different fermentation degrees (under-fermented, normally fermented, and over-fermented). Significant differences in physicochemical properties were observed among fermentation degrees, particularly in acidity and reducing sugar content. Electronic nose analysis revealed distinct sensor response patterns among different fermentation degrees, indicating differences in overall volatile odor fingerprint patterns. A total of 81 volatile compounds were identified by HS-SPME-GC-MS, with aldehydes, ketones, and pyrazines showing pronounced variations among fermentation degrees, and acetaldehyde exhibiting strong discriminatory potential. LEfSe analysis identified 18 microbial taxa as potential biomarkers associated with different fermentation degrees, including Pichia kudriavzevii, Lentibacillus daiqui, and Acetobacter pasteurianus. Correlation analysis revealed significant positive associations between acetaldehyde levels and Acetobacter abundance. Furthermore, KEGG, CAZy, and eggNOG analyses revealed differentiated functional potentials among fermentation degrees, providing insights into the potential metabolic basis associated with flavor differentiation. Overall, these findings highlight that fermentation degree differentiation is closely associated with coordinated changes in physicochemical conditions, microbial communities, and functional potentials, providing ecological insights into flavor differentiation and theoretical support for objective fermentation degree evaluation and quality control of Jiangxiangxing Baijiu production.

Fermentation↗