Although microaerobic activated sludge process has been developed to convert nitrogen compounds in fermentation industrial wastewater to ammonium, its applicability at the industrially relevant situations is largely unknown. Here, responses of the treatment performances and microbial communities to the sequential settings of the low DO and/or low pH conditions for the effective start-up of the practically downscaled processes fed with the simulated wastewater derived from real streams were investigated by the 3D fluorescence spectroscopy, LC-TOF-MS, qPCR of nitrification-related functional genes and high-throughput sequencing of 16S/18S rRNA genes. The ammonium conversion and retention rates increased rapidly to 77.6-103.6% in all the settings, highlighting the relevance of the process in the context of nitrogen recovery with a combination of the downstream concentration and separation steps in practical application. The advancement was associated with the formation of the stable whole prokaryotic communities and the decline (∼2 order decreases in gene copies) of the abundant nitrifier ammonia-oxidizing bacteria (AOB). The hydrolytic and/or proteolytic Extensimonas soli and Thermomonas haemolytica commonly proliferated and predominated at ∼51.1% and ∼26.5% of the total, suggesting their central involvements in ammonification. The TOC removal rates were relatively high (>88.3%), although exhibiting the trade-off relationship with nitrogen recovery, for which some dissolved organic residues, e.g., amino acid-related substances, were identified. An issue of this process was the increases in the treated wastewater turbidities but could be alleviated by DO control. The eukaryotes Amoebozoa and Ochrophyta, as well as AOB, were responsible for the sludge flocculation, owing to the comparison with the conventional nitrification-denitrification process with the low turbidities. Consequently, this study illuminated the high adaptabilities of microbial communities to the changing conditions for establishment of the novel nitrogen circular technology.
This study investigated the feasibility of accelerating aerobic granular sludge (AGS) formation by short-term in-situ dosing of chitosan (CTS) in sequencing batch reactors. Compared with the control reactor, short-term CTS dosing during start-up markedly promoted biomass aggregation, shortened the complete granulation time from 27 to 9 days, and produced larger and denser granules with superior settleability. In the CTS-amended reactor, the mean granule size reached 752 µm, while sludge volume index at 30 min (SVI30) rapidly decreased to 59.4 mL/g within 24 h and stabilized at 38.2-44.8 mL/g during steady operation. Mechanistic analyses showed that CTS reduced sludge surface electronegativity, stimulated extracellular polymeric substance (EPS) secretion, and functioned as both a cationic bridging agent and a physical nucleation core. These roles were further supported by direct visualization using CTS-coated Fe3 O4 particles as a tracer. Temporary pH control applied to activate the cationic properties of CTS initially suppressed nitrification, but the inhibition was reversible. After recovery, the CTS reactor achieved superior total nitrogen (TN) removal, with an average effluent TN of 8.6 mg/L compared with 15.3 mg/L in the control. The enhanced TN removal may be associated with the combined effects of larger and more compact granules and the possible contribution of residual CTS as supplementary electron donor. Microbial analysis further revealed the enrichment of EPS-producing and structural bacteria, especially Thiothrix, in the CTS reactor. Overall, short-term in-situ CTS dosing provides a simple and biocompatible strategy for rapid AGS start-up and improved nitrogen removal.
Highly crystalline single-walled carbon nanotubes were employed as robust supports for carbon-encapsulated PtPd alloy electrocatalysts synthesized via a rapid, industrially scalable solution plasma method, enhancing long-term durability under frequent start-up/shut-down conditions. Electrochemical evaluation of PtPd@C/SWCNT as a cathode catalyst in a polymer electrolyte fuel cell (PEFC) membrane electrode assembly (MEA) demonstrated superior durability compared to commercial Pt/C and monometallic Pt@C/SWCNT under an accelerated durability test. PtPd@C/SWCNT maintained high performance for 5000 potential cycles and retained over 50% of its electrochemically active surface area (ECSA) after 10 000 cycles under the accelerated durability test of high-potential triangular pulses (1.0-1.5 V) simulating harsh conditions encountered during actual start-up/shut-down operations. Carbon encapsulation effectively inhibited nanoparticle agglomeration and suppressed the oxidation of the SWCNT support in close proximity to the nanoparticles during the durability test of 30 000 cycles. Raman spectroscopy confirmed the excellent corrosion resistance and maintained the crystallinity of the SWCNT support. The negligible thickness change observed in the PtPd@C/SWCNT cathode layer further highlights the benefit of the SWCNT support and carbon encapsulation in maintaining structural integrity under severe operating conditions. XPS analysis indicated a more stable, reduced state of Pt in PtPd@C/SWCNT compared to that of Pt/C. These results highlight the synergistic effects of the SWCNT support and carbon encapsulation in improving both catalyst stability and support durability for prolonged PEFC operation, particularly under demanding heavy-duty vehicle (HDV) conditions.
Biological methane (CH4) removal from dilute air streams (200-5,000 ppm) is fundamentally constrained by slow mass transfer, primarily due to CH4's low solubility and diffusivity in water. Conventional biofilters, the current state of technology, suffer from long start-up periods, progressive pore clogging, and high transport energy requirements. This study investigates a "dry" biofilm concept designed to overcome these bottlenecks by minimizing the aqueous boundary layer while maintaining microbial activity via capillary-mediated nutrient delivery. Using concentrated biomass of the methanotroph Methylomicrobium buryatense 5GB1C, three generations of membrane-supported reactor configurations were evaluated. The third-generation (G3) design utilized a cellulose-bead capillary support to maintain a physical gap between the membrane and the liquid surface, enabling continuous drainage of metabolic water. Experimental results demonstrated that the "dry" G3 configuration achieved CH4 removal rate of 148.9 mg·m-2·hr-1 at 4000 ppm, representing a 397% improvement over the first-generation floating mesh configuration. At 500 ppm, G3 design achieved a CH4 removal rate of 18.6 mg·m-2·hr-1, corresponding to an over six-fold improvement over one of the highest reported values. Furthermore, the system enabled immediate start-up post-inoculation and maintained an optimal microenvironment pH (8.8-9.0) even as the bulk medium acidified. These results establish that replacing liquid-phase diffusion with drastically faster gas-phase transport provides a high-efficiency framework for mitigating low-concentration CH4 emissions. With the added benefits of minimal pressure drop and easy biomass harvesting via scraping, this dry biofilm approach offers a scalable and sustainable alternative for atmospheric methane mitigation.
We report the mechanism underlying two-step yielding in repulsive colloidal microgel glasses under shear deformation. Strain sweep and start-up flow experiments demonstrate the existence of two-step yielding, which was further investigated by creep-recovery, and Lissajous-Bowditch curves to probe intra-cycle nonlinearities. By increasing the microgel volume fraction, we track the transition from entropic to jammed glass regimes and examine the distinct roles of particle softness and crosslinking heterogeneity in yielding behaviour. Soft core-shell particles exhibit two-step yielding in the jammed glass regime at a frequency of ω = 1 rad s-1. We compare the results for three types of particles: soft core-shell; stiff core-shell; and homogeneously crosslinked. We find that stiff core-shell and homogeneous particles do not exhibit two-step yielding under any experimental conditions. These findings demonstrate that softness combined with a core-shell particle structure is necessary to support two-step yielding. Intra-cycle nonlinearities reveal that strain stiffening develops between the first and second yield points, arising from resistance to macroscopic flow at and beyond the first G″ peak. This resistance to cage breaking originates from the strong interlocking of interpenetrated polymer chains that occurs during microgel deformation and compression in the jammed state. Macroscopic flow begins at the second yield point, where particles escape their cages by breaking the interlocking structure, leading to the G'-G″ crossover.
Microplastics may disturb microbial activity and biofilm development in biological wastewater treatment systems, yet the response of three-dimensional rotating biological contactor start-up biofilms to polypropylene microplastic stress remains unclear. This study evaluated a biofilm initiation strategy using heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria (H-3D-RBCs) and compared it with activated sludge-inoculated systems (A-3D-RBCs) under polypropylene microplastic (PP-MP) exposure. H-3D-RBCs showed superior resistance to PP-MP disturbance, with total nitrogen removal decreasing by only 14 %, compared with an approximately 60 % decline in A-3D-RBCs. Respiratory activity inhibition remained below 15 % in H-3D-RBCs but exceeded 90 % in A-3D-RBCs. 16S rRNA gene sequencing showed that PP-MP reduced species richness and diversity in A-3D-RBCs and was associated with a > 90 % loss of core denitrifying genera, including Corynebacterium and Pseudoxanthomonas, whereas H-3D-RBCs maintained community stability and enriched Pseudoxanthomonas to 13.8 %. Metagenomic analysis indicated that PP-MP impaired nitrification and denitrification potential in A-3D-RBCs, as reflected by decreased genes encoding AMO and HAO, a 51.78 % decrease in nosZ abundance, and enhanced dissimilatory nitrate reduction to ammonium (DNRA), which likely intensified competition with denitrification and promoted nitrogen conversion to ammonia. In contrast, H-3D-RBCs suppressed DNRA and maintained high nosZ abundance. Untargeted metabolomics further showed that PP-MP was associated with metabolic disorders in A-3D-RBCs, especially disruptions in alanine, aspartate, and glutamate metabolism and arginine biosynthesis, whereas H-3D-RBCs preserved these key nitrogen metabolic processes. Overall, this study identifies key vulnerabilities of nitrogen-removal biofilms under PP-MP disturbance and provides multi-omics evidence to support the development of microplastic-resistant biofilm wastewater treatment systems.
The REvascularization CHoices Among Under-Represented Groups Evaluation (RECHARGE) program is enrolling 1200 women, Black, and Hispanic patients in two parallel randomized trials of percutaneous coronary intervention (PCI) versus coronary artery bypass grafting (CABG). Funded by a phased Patient-Centered Outcomes Research Institute award, the pilot phase was designed to assess the feasibility of enrolling groups historically under-represented and challenging to enroll in prior revascularization trials, evaluate willingness of patients to accept randomization, refine patient and stakeholder engagement, and scale site infrastructure and data collection across diverse centers. We report key insights from the pilot phase. Physician and patient treatment preferences, often shaped by prior experience and evidence not directly applicable to these cohorts, were the main reasons eligible patients were not randomized. Many sites also lacked consistent multidisciplinary Heart Team processes for coronary disease, requiring new workflows to establish equipoise between PCI and CABG. Successful recruitment required intentional trust-building and tailored patient-facing materials, while engagement of non-academic centers demanded added financial, educational, and start-up support. During the 2-year pilot phase, 91 U.S. and 17 Canadian sites were selected, and 65 were activated. Median activation time was 10.8 months (IQR 9.1-13.6). The pilot enrollment goal of 60 participants was exceeded, with 141 patients randomized within 13 months at a mean rate of 0.27 patients/site/month, prompting expansion to up to 150 sites for the full program. The lessons learned from the pilot phase of the RECHARGE program can inform the design and implementation of future randomized trials seeking to enroll traditionally under-represented populations.
A comprehensive review and analysis of drug discovery efforts at Indian companies between the mid-1990s and 2025 reveals 1095 ongoing (462) or past (633) projects and molecules under investigation at 195 major pharmaceutical, biotechnology, and start-up companies, of which 128 are currently actively pursuing research. They consist mainly of small molecules (811), followed by novel biologics (189) and gene therapies (95), and cover all stages from early discovery (618), preclinical (276), and clinical development phases (Phase 1: 98; Phase 2: 63; Phase 3: 19) up to approved drugs and treatments (21). Small molecules are dominated by new chemical entities (684), followed by prodrugs, salts, and formulations (56), repurposed drugs (53), and others (18). Biologics consist largely of monoclonal antibodies or fragments (92), antibody-drug conjugates (22), various (fusion) proteins (40), enzymes (10), and others (25). Gene therapies use gene silencing (24), gene transfer (21), genome editing (7), modulation of mRNA splicing (3), and genetically modified cells based on chimeric antigen receptor technology using T and NK cells (40). Tracking companies and projects over time illustrates the dynamics and increasing diversification of drug discovery activities in India.
Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinical burden and are challenging to model using conventional experimental systems. Pancreatic organoids provide physiologically relevant three-dimensional models that recapitulate the key structural and functional features of the human pancreas. This review summarizes recent advances in pancreatic organoid and organoid-on-a-chip technologies, focusing on their applications in pancreatic disease modeling, therapeutic screening, and potential relevance to personalized treatment. This review discusses the construction of pancreatic organoids and pancreatic organoid-on-a-chip platforms, including key culture parameters and practical strategies for model establishment. The applications of pancreatic organoids in modeling pancreatic cancer, diabetes, pancreatitis, and cystic fibrosis are reviewed. In addition, the role of patient-derived organoids in therapeutic screening and their potential integration into personalized treatment workflows are evaluated. The enabling role of artificial intelligence in pancreatic organoid research is also discussed, followed by an overview of the current challenges and future perspectives. Pancreatic organoid-based platforms are promising tools for pancreatic disease modeling and therapeutic evaluation. Addressing the current limitations of cellular complexity, vascularization, reproducibility, and scalability is essential for expanding their research and clinical utility.
In the era of artificial intelligence, machines are demonstrating an unprecedented capacity to learn from massive amounts of real-world data to perform human-like cognitive processes, enabling them to recognize environments, objects, and conditions and make critical decisions more accurately than ever. In the medical field, the potential to generate realistic, privacy-preserving, unbiased synthetic data can be the key to unlocking the potential of artificial intelligence in medicine and overcoming the current barriers such as data privacy concerns and high data curation costs. Advanced data-driven solutions could lead towards more robust clinical decision support systems and enhanced clinical training. This Perspective critically examines current and emerging advances in synthetic data generation, and highlights its anticipated transformational effect for early and efficient prevention, diagnosis and treatment of gastrointestinal diseases. Research challenges and directions are identified for leveraging the benefits of synthetic data as well as translating and adopting them in clinical workflows.
Despite numerous studies have examined the impact of obesity and poor nutritional status on the prognosis of critically ill patients, their relationship with sepsis-associated acute kidney injury (SA-AKI), particularly in older patients, remains unclear. This study aimed to investigate the association between obesity, nutritional status, and early SA-AKI in older patients with sepsis. This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Older septic patients without pre-existing chronic kidney disease (CKD) stage 3-5 or elevated serum creatinine (SCr; >1.2 mg/dL in males and > 1.1 mg/dL in females) at admission were included to focus on incident early SA-AKI. Obesity was measured by body mass index (BMI), and nutritional status was evaluated using the Geriatric Nutritional Risk Index (GNRI) and Prognostic Nutritional Index (PNI) as prognostic indicators. Multivariable logistic regression, multivariable fractional polynomial regression, and restricted cubic spline models were applied to analyze the associations between BMI, GNRI, PNI, and early SA-AKI. Additionally, the relationships between BMI, GNRI, PNI and outcomes were examined in early SA-AKI patients. Among 4238 older septic ICU patients without pre-existing renal dysfunction, approximately 50% developed SA-AKI within 48 h of Intensive care unit (ICU) admission. Overweight (adjusted odds ratio [AOR] 1.39, 95% confidence interval [CI] 1.17-1.65), obese (AOR 1.95, 95% CI 1.63-2.35), and severely obese (AOR 2.19, 95% CI 1.63-2.94) were associated with higher odds of early SA-AKI risk compared with normal weight patients, while each 1-point increase in BMI raised risk of early SA-AKI by 4% (95% CI 1.03-1.05). No significant correlations were found between GNRI or PNI and early SA-AKI risk. Among older patients with early SA-AKI, underweight (In-hospital mortality: AOR 2.06, 95% CI 1.07-3.96; 6-month mortality: adjusted hazard ratio [AHR] 1.88, 95% CI 1.21-2.92) and lower GNRI (In-hospital mortality: AOR 1.80, 95% CI 1.00-3.22; 6-month mortality: AHR 1.38, 95% CI 1.06-1.81) predicted higher in-hospital and 6-month mortality. In a selected cohort of older septic ICU patients without pre-existing renal dysfunction, overweight and obesity are associated with a higher risk of early SA-AKI. Once SA-AKI develops, underweight and poorer nutritional status are related to worse survival. These findings highlight the prognostic value of BMI and nutritional indices for risk stratification in this population. Further prospective studies are warranted to validate these associations and explore their potential implications for risk stratification.
Major depressive disorder (MDD) is increasingly linked to astrocyte dysfunction, yet how systemic metabolic disturbances contribute to glial alterations remains unclear. Disruption of trace metal homeostasis, particularly copper, has emerged as a potential contributor, but the underlying cellular mechanisms are poorly defined. Here, we integrate clinical data and mouse models to investigate the role of copper dyshomeostasis in depression. We show that copper levels are elevated in the systemic circulation of patients with MDD and in the prelimbic cortex (PrL) of stressed mice. In mice, copper accumulation is associated with increased astrocytic ferredoxin 1 (FDX1) expression, accompanied by reduced astrocyte number and structural complexity, impaired calcium signaling, and disrupted excitatory synaptic function. Astrocyte-specific manipulations, in vivo calcium imaging, and electrophysiological recordings demonstrate that astrocytic FDX1 mediates the effects of copper imbalance on neural circuit dysfunction. Notably, physical exercise restores copper homeostasis, normalizes astrocytic FDX1 expression, improves astrocyte-neuron coupling, and alleviates depressive-like behaviors. These findings identify an astrocyte-mediated mechanism through which systemic copper imbalance influences neural function and provide insight into how exercise may mitigate depression-related neural dysfunction.
Elevated methylglyoxal (MG) levels contribute to diabetes-related complications through accelerated formation of advanced glycation end products, highlighting the need for sensitive, portable, and non-invasive monitoring strategies. Here, we report a gold-carbon nanohybrid electrochemical MG sensor integrated into a three-dimensional printed microfluidic platform for point-of-care analysis. The sensing interface is engineered by sequential electrodeposition of carboxyl-functionalized multi-walled carbon nanotubes (fMWCNT) and gold nanoparticles (AuNPs) onto a screen-printed carbon electrode (SPCE). The fMWCNT provide a high-surface-area scaffold for MG adsorption, while AuNPs enhance electroactive surface area and electron transfer. The AuNPs/fMWCNT/SPCE sensor exhibits quasi-reversible electrochemical behavior suitable for sensitive MG detection. The optimized sensor achieves reliable MG detection across a wide dynamic range (50 nM-100 µM), with limits of detection of 40 nM in aqueous media and 400 nM in artificial sweat. Accurate MG quantification in human saliva and sweat, with recoveries exceeding 97%, demonstrates robustness in complex biological matrices. This work demonstrates the potential of this electrochemical sensing system for future wearable biosensors and personalized diabetes monitoring.
Screening for diabetic retinopathy using fundus photographs is the global standard of care but results in high false-positive referrals to evaluate diabetic macular edema (DME), placing a substantial burden on specialist eye clinics. Integrating an AI-based optical coherence tomography (AI-OCT) system into screening pathways may reduce potentially unnecessary referrals. To evaluate the diagnostic and referral performance of an AI-OCT system for DME detection within a diabetic retinopathy screening pathway in clinical settings. Stepwise evaluation conducted in Hong Kong Special Administrative Region: a prospective silent-mode validation (February 2020 to July 2023) recruiting 603 patients with diabetes at a tertiary hospital triage unit, followed by a multicenter noninferiority RCT (September 2023 to April 2025), with follow-up completed in May 2025, recruiting 276 patients with suspected DME referred from a territory-wide diabetic retinopathy screening program. RCT participants were randomized to intervention (referral for DME evaluation based on both fundus photograph-based screening reports and AI-OCT reports [n = 137]) or control (automatic referral based solely on fundus photograph-based screening reports [n = 139]) groups. The AI-OCT system incorporated image-quality assessment, DME detection, and uncertainty flagging. Study outcomes focused on referral rates under the 2 pathways; for ethical reasons, all participants ultimately underwent specialist evaluation. The primary outcome was false-positive DME referral rate, with a prespecified noninferiority margin of 20%. The secondary outcomes included sensitivity and specificity for DME detection and DME referral. In prospective silent-mode validation (mean age, 64.7 [SD, 9.4] years; 56.2% male), 86 of 1200 scans (7.2%) were identified as ungradable and 49 of 1114 gradable scans (4.4%) were classified as uncertain. The system achieved 98.8% (95% CI, 94.5%-100.0%) sensitivity and 90.7% (95% CI, 88.7%-92.4%) specificity for DME detection. In the RCT (mean age, 63.9 [SD, 10.9] years; 54.7% male), DME prevalence was similar in the intervention and control groups (30.9% vs 29.9%). The false-positive DME referral rate was 24.1% (95% CI, 14.6%-37.0%) and 69.1% (95% CI, 61.0%-76.1%), respectively (absolute difference, -45% [95% CI, -58.2% to -31.9%; P < .001 for noninferiority]; upper bound of the CI below the prespecified noninferiority margin of 20%). Sensitivity for DME referral was 100.0% (95% CI, 100.0%-100.0%) in both groups. Specificity for DME referral was 86.5% (95% CI, 79.3%-92.9%) in the intervention group and 0.0% (95% CI, 0.0%-0.0%) in the control group. No cases of DME occurred among nonreferred participants in the intervention group. Compared with standard practice, incorporation of the AI-OCT system as a secondary screening tool was noninferior with respect to false-positive referral rates and was associated with a substantial reduction in potentially unnecessary DME referrals without compromising sensitivity. Chinese Clinical Trial Registry: ChiCTR2300075087.
Aggregates, fundamental structural units of soils, significantly affect soil organic carbon (SOC) dynamics. Yet, how tree mycorrhizal types drive SOC dynamics at the aggregate scale remains unclear. We investigated the impacts of three arbuscular mycorrhizal (AM) and three ectomycorrhizal (ECM) tree species on SOC and its particulate fractions, and the roles of metal elements and microbial communities across aggregate size classes in a temperate forest. AM tree soils exhibited higher macroaggregate proportions and aggregate stability, as well as increased contents of SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) across all aggregate size fractions than those under ECM trees. AM tree soils were richer in Ca, Mg, Fe, and Al, whereas ECM tree soils had higher K and Na. Mycorrhizal types and aggregate size (macroaggregates vs microaggregates) directly or indirectly affected SOC fractions through soil metal elements, total nitrogen (TN), fungal richness, and Bacteroidota abundance. Our study demonstrates that AM and ECM trees influence SOC dynamics through aggregate-size-dependent changes in soil TN, metal elements, and microbial traits, and that Ca, fungal richness, and Bacteroidota are particularly influential mediators. These findings underscore the importance of incorporating tree mycorrhizal traits into SOC dynamics under global change.
In passive-source seismic exploration, even after seismic instruments complete unified start-up acquisition and hardware synchronization, long-duration continuous records may still contain small residual timing errors, which in turn broaden cross-correlation peaks and degrade event-location results. To address this problem, this study proposes a wavefield-domain residual timing refinement method. The method uses stable noise windows and controlled artificial events in continuous records as constraints, and performs data-window preprocessing, reference cross-correlation function construction, pairwise residual lag estimation, confidence-weighted multi-station joint fusion, and smoothing-constrained fitting of a continuous correction curve to achieve a posterior refinement of residual timing errors after hardware synchronization. Fractional-delay interpolation is then used for waveform correction. Validation using a 60 min continuous record from a local six-station array shows that the proposed method can serve as an effective supplement to hardware synchronization, suppress residual timing errors, and improve the temporal consistency, waveform stackability, and interpretation reliability of passive-source seismic exploration data.
Soil contamination by microplastics (MPs) and heavy metals (HMs), particularly cadmium (Cd), poses an emerging threat to agricultural sustainability, food safety, and human health. Although the individual effects of MPs and Cd on crop performance have been widely investigated, their interactive impacts on rice remain poorly understood. Biochar (BC) and melatonin (MT) have recently attracted attention for their capacity to alleviate HM toxicity and abiotic stress in plants; however, their combined potential to mitigate MP-Cd co-stress has not yet been explored. This study aimed to evaluate the individual and enhanced effects of BC and MT on rice growth, physiological and molecular responses, Cd bioavailability, and soil properties under MP-Cd co-contamination. Exposure to Cd (20 mg kg⁻¹) and MPs (1%) significantly inhibited rice growth and productivity by inducing oxidative stress, enhancing Cd uptake and accumulation, suppressing chlorophyll biosynthesis, and impairing water and nutrient acquisition. In contrast, the combined application of BC (2%) and MT (100 µM) markedly alleviated these adverse effects and outperformed individual amendments. Co-application substantially increased chlorophyll content (82%), leaf relative water content (48.47%), antioxidant enzyme activities (57.84-99.42%), proline accumulation (49.21%), and endogenous melatonin (EM) levels (48.35%). At the molecular level, BC + MT treatment upregulated antioxidant-related genes (OsAPx6, OsCAT, OsPOD, and OsSOD), the proline biosynthesis gene OsP5CS, and the MT biosynthesis gene OsCOMT, while significantly downregulating Cd transporter genes (OsNRAMP1 and OsHMA3). Furthermore, this combined treatment reduced soil Cd bioavailability and Cd accumulation in rice tissues, while improving soil fertility by increasing nitrogen (N), phosphorus (P), potassium (K), and soil organic carbon (SOC). This study provides the first evidence that the combined application of BC and MT effectively mitigates the detrimental effects of simultaneous MP and Cd contamination in rice. The enhanced physiological, molecular, and soil-level improvements induced by BC and MT collectively enhance rice growth and productivity under MP-Cd stress. These findings highlight a promising, integrated remediation strategy to manage co-pollution of MPs and HMs in agricultural soils, with important implications for sustainable crop production and food security.
Bacterial biofilms serve as innate protective frameworks, sheltering embedded microbes to sustain their survival while potentially enhancing antimicrobial resistance by blocking bactericide penetration. While extensive research has focused on planktonic bacteria, biofilm recalcitrance remains understudied, leaving conventional antimicrobials often ineffective against these sessile communities. This underscores an urgent need for next-generation agents with specialized biofilm-inhibiting properties to enabling effective antibacterial therapy; in this study, we used pyrrolidine-3-carboxylic acid as a starting material to synthesize a series of sulfonamide-containing pyrrolidine derivatives via an active coupling reaction. Compound A14 exhibited the strongest activity, with an EC₅₀ value of 38.33 μg/mL against Xanthomonas oryzae pv. oryzae (Xoo). Mechanistic investigations revealed that A14 suppresses extracellular polymeric substance (EPS) biosynthesis and bacterial motility-key drivers of pathogenicity, biofilm development, and plant cell wall degradation. Conductivity measurements and protein leakage assays confirmed that A14 disrupts multiple Xoo physiological functions, positioning it as a promising antimicrobial candidate targeting core mechanisms of bacterial plant diseases.
Restoring load-bearing connective tissues (bone, cartilage, tendon, and ligament) remains a central challenge in regenerative medicine. While autografts and synthetic grafts provide temporary solutions, they are hampered by donor-site morbidity, immune complications, and poor long-term stability. Hydrogels, with their extracellular matrix-like architecture and biocompatibility, have emerged as versatile scaffolds for regeneration. Yet their intrinsic mechanical fragility has limited clinical use in mechanically demanding environments. Recently, both intrinsic and biomimetic reinforcement strategies have advanced hydrogel mechanics, while composition-structure designs incorporating bio-functional components and tailored architectures have expanded their therapeutic scope. However, the complexity of native tissues renders single-strategy solutions insufficient to simultaneously achieve robust mechanics, functional bioactivity, and physiological adaptability. This review uniquely consolidates mechanical reinforcement and bio-functional design strategies for biomass-derived hydrogels, emphasizing integrative concepts that couple macroscopic architecture, dynamic bonding, interfacial engineering, and multiphase doping. By framing hydrogel development through a cross-strategy and systems perspective, this article addresses a critical gap in the field and highlights a rational pathway toward next-generation scaffolds. Looking forward, stimuli-responsive hydrogels with adaptability, gradient, and multiphasic architectures, and AI-guided optimization are set to redefine design. Integrating materials science, biomechanics, and computational intelligence will yield patient-specific, translatable hydrogels with strong mechanics and regenerative efficacy.
Ovarian cancer is a gynecological malignancy associated with high mortality and poses significant clinical challenges in early diagnosis and precision treatment. Although the rapid advancement of artificial intelligence (AI) has introduced novel approaches to this field, a comprehensive bibliometric overview remains lacking. This study aims to fill this gap by providing a systematic bibliometric analysis of this rapidly evolving domain. In this study, the Web of Science Core Collection (WoSCC) was used to retrieve literature on AI applications in ovarian cancer research published from 2006 to the search date (November 19, 2025). Using CiteSpace and VOSviewer, we conducted visual and quantitative analyses of publication trends, countries/regions, institutions, authors, journals, highly cited papers, and keywords. A total of 786 publications were included in the analysis. The annual publication output showed pronounced exponential growth, with a marked acceleration after 2019. China, the United States, and the United Kingdom were the leading contributing countries. Research hotspots centered on AI-assisted diagnosis, prognostic prediction models, radiomics, and biomarker discovery. The evolution of keywords indicated that frontier research has shifted from basic classification toward more advanced areas, including high-grade serous ovarian carcinoma, multimodal learning, and explainable AI. Research on AI in ovarian cancer has progressed rapidly, with international collaboration concentrated among leading contributors such as China, the USA, and the UK. Future efforts should prioritize the development of explainable and robust clinical AI systems, deeper integration of multimodal data, closer collaboration between clinicians and AI researchers, and high-quality data sharing to facilitate the translation of research findings into precise clinical practice.