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Anaerobic ammonium oxidation (anammox) is a promising low-carbon pathway for nitrogen removal, but its actual application is still limited in some scenes due to unstable nitrite supply, nitrite-oxidizing bacteria (NOB) proliferation, biomass washout, and low nitrogen removal rate. This review summarizes the crucial issues governing practical anammox application. The kinetic mismatch between partial nitritation-derived nitrite production and anammox consumption is discussed together with the operational differences between one-stage and two-stage partial nitritation/anammox configurations, including nitrite accumulation, pH/alkalinity balance, free ammonia (FA)/free nitrous acid (FNA) dynamics, NOB control, and nitrous oxide emission risk. For reactor management, real-time control guided by monitoring of nitrogen species is proposed as a more reliable strategy than control based on fixed dissolved oxygen setpoints, whereas FA/FNA inhibition alone is insufficient under mainstream conditions. Inhibition diagnosis, staged startup, phosphorus recovery, engineered third sludge, and high-rate mainstream anammox are further evaluated. This review provides practical perspectives for developing mainstream anammox into a stable and reliable low-carbon wastewater treatment process.

Learning agility has become an essential capability for employees working in technology-driven environments characterized by rapid change and uncertainty. Despite increasing attention on learning agility, limited empirical research has examined how different levels of cognitive abilities contribute to its development, particularly among Generation Z employees. This study investigates the cognitive determinants of learning agility by distinguishing between basic cognitive abilities and high-level cognitive abilities and examining their roles across established and start-up companies. A total of 270 Generation Z employees in Indonesia participated in the study, consisting of 135 employees from established companies and 135 from start-up companies. Cognitive abilities were assessed using objective psychometric instruments, where basic cognitive abilities (reasoning, memory, attention, coordination, and perception) were measured using CogniFit, while high-level cognitive abilities were assessed through the Divergent Association Task (DAT) for creativity, the Watson-Glaser Critical Thinking Appraisal for critical thinking, and the FourSight framework for problem-solving. Learning agility was measured using a multidimensional behavioral scale. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that higher-order cognitive abilities play a more prominent role in shaping learning agility than basic cognitive abilities. Creativity and problem solving consistently demonstrate significant positive relationships with learning agility across organizational contexts, while reasoning, critical thinking, and perception show context-dependent effects across organizational environments. These findings suggest that learning agility is primarily driven by generative and evaluative cognitive processes rather than by basic cognitive efficiency alone. The study contributes to a deeper understanding of the cognitive architecture of learning agility and provides insights for organizations seeking to develop adaptive talent in rapidly evolving technological environments.

Oral diseases represent a major global health burden, underscoring the need for sensitive, accessible, and noninvasive diagnostic technologies. Electrochemical biosensing offers a powerful route for point-of-care oral health monitoring by translating biomolecular interactions in saliva, gingival crevicular fluid, and exhaled breath condensate into quantifiable electrical signals. This review systematically discusses electrochemical biosensing strategies for oral disease diagnosis, beginning with the structure and operating mechanisms of electrochemical sensors and then summarizing their applications in detecting disease-related nucleic acids, proteins, pathogens, and other molecules. We further examine feasible strategies for early diagnosis, including signal amplification methods based on nanomaterials, enzyme catalysis, nucleic acid amplification, chemical deposition, and cascade integration, as well as antifouling interfaces designed to maintain stable sensing performance in complex oral biofluids. Particular attention is given to advanced transistor architectures, especially organic electrochemical transistors (OECTs), which offer intrinsic signal amplification and high-gain readout for low-abundance biomarkers. Finally, we outline current challenges, future directions, and translational opportunities for electrochemical biosensing technologies, providing a roadmap toward precision dentistry and modern oral health management.

To advance stunting reduction efforts, robust cost evidence on multisectoral interventions is essential for optimising resources and achieving outcomes efficiently. This study objective was to estimate the cost and cost-efficiency of three multisector interventions that combined standard of care (SOC), SOC plus cash transfers (SOC+CT) and SOC plus nutritional supplementation (SOC+NS) with small quantity lipid-based nutrient supplements (SQ-LNS) complemented with a family food ration (FFR) to prevent stunting in Southern Angola. Cost-effectiveness will be estimated in a future study. Interventions were nested within the Mother and Child Chronic Undernutrition in Angola (MuCCUA) trial and costs estimated after 1 year of implementation. Using a mixed-methods approach, we estimated financial and economic costs from institutional and societal perspectives. Activity-based costing and the ingredients approach were used to capture and calculate total institutional costs and opportunity costs incurred by participants. Cost-efficiency of each intervention was calculated as cost per participant reached. Costs were adjusted for inflation and reported in 2024 USD. Through the first 12-month implementation period, MuCCUA interventions covered 1423 pregnant women and their children. Total institutional costs by intervention arms were: US$190 408 for SOC (n=463), US$414 946 for SOC+CT (n=479) and US$515 799 for SOC+NS (n=481). Total monthly costs per participant reached were US$12.85, US$27.07 and US$33.51, respectively, for SOC, SOC+CT and SOC+NS (societal costs: US$13.48, US$28.26, US$34.56). Personnel (73%) and staff training (24%) drove most costs in SOC, whereas supplies-cash transfers (32%) in SOC+CT and SQ-LNS with FFR (35%) in SOC+NS-were the largest expenses. Intervention costs were higher in the SOC+CT and SOC+NS arms driven by inputs and recurrent distributions of cash transfers and nutrition supplies and lower in the SOC driven by personnel and training. Cost-efficiency could be increased by increasing the implementation period, so that start-up costs are spread over a long period of time. Disaggregated costing enhances understanding of resource use in complex programmes, supporting operational planning and budgeting, and will inform forthcoming cost-effectiveness analyses once outcome data become available. NCT05571280.

Multidrug resistance and invasive metastasis constitute pivotal clinical bottlenecks that severely compromise curative outcomes of malignant tumors. Conventional chemotherapy and immunotherapy frequently fail to achieve satisfactory efficacy due to drug resistance barriers and tumor immune escape. Herein, a hyaluronic acid‑cinnamaldehyde Schiff base micelle nanoplatform loading quaternary ammonium‑modified carbon dots (HACA@QASCDs) is rationally constructed, which achieves targeted killing of drug‑resistant tumor cells, remodeling of immunosuppressive microenvironments, and inhibition of distant metastasis via a sequential cascade of irreversible membrane perforation, mitochondria‑dependent apoptosis, and immunogenic cell death (ICD). HACA@QASCDs actively accumulate in drug‑resistant CT26 (DR‑CT26) cells through HA‑CD44 recognition and enable pH‑triggered QASCDs release in acidic tumor microenvironments. The liberated QASCDs elicit irreversible membrane perforation, leading to lactate dehydrogenase leakage, disrupted calcium homeostasis, mitochondrial depolarization, and subsequent intrinsic apoptosis. Such membrane damage simultaneously ignites ICD, and the released damage‑associated molecular patterns effectively drive dendritic cell maturation and M2‑to‑M1 macrophage polarization. In vivo evaluations in bilateral syngeneic tumor models reveal that HACA@QASCDs alone yields 54.2% primary tumor inhibition and 28.6% distant tumor inhibition. Upon combination with αPD‑L1, the distant tumor inhibition rate is markedly elevated to 68.7%. By integrating membrane perforation‑mediated direct cytotoxicity and ICD‑evoked immune activation, HACA@QASCDs offers a highly potent and clinically translatable synergistic strategy to surmount tumor multidrug resistance and block invasive metastasis.

Direct-to-consumer (DTC) pharmacogenomic (PGx) testing is expanding rapidly in the UK, yet no dedicated regulatory framework currently governs these services. Although a 2021 parliamentary inquiry recommended stronger safeguards and clearer technical standards for genomic testing, these proposals have not been applied to PGx. This study explores public attitudes toward DTC PGx testing, focusing on expectations for pre-test information, quality standards, and NHS data use. We conducted focus groups with members of the public, both with and without prior experience of purchasing DTC PGx tests, or other online health tests. Focus groups were audio-recorded with consent, transcribed, and analysed thematically. We identified three themes: (mis)understanding towards and awareness of DTC PGx testing; altruistic motivation and equity concerns; and (mis)trust. Participants were generally enthusiastic about PGx testing, as long as issues of equity, data protection, and regulation were addressed, with data sharing concerns being particularly prominent.

Ischemic stroke results from the occlusion of a cerebral artery and is a leading cause of mortality and disability worldwide. Multimodal computed tomography (CT), including CT perfusion (CTP) and CT angiography, is crucial to acute stroke evaluation but involves higher radiation exposure than non-contrast CT due to repeated volumetric imaging. Reducing CTP radiation dose without losing image quality remains important challenge. This study proposes a machine-learning-based denoising autoencoder (DAE) to reduce noise introduced by dose reduction while preserving the quality of CTP images and perfusion parameter maps. CTP images from 48 acute ischemic stroke patients from the PRove-IT trial were used. Low-dose conditions were simulated by adding Gaussian and Poisson noise at varying strengths, with Poisson noise applied in the sinogram domain and Gaussian noise in the image domain. The DAE was trained using paired noisy and original images. Performance was evaluated by assessing structural similarity of CTP source images and perfusion maps, as well as clinical accuracy based on infarct core volumes derived from cerebral blood flow maps. The DAE restored strong structural similarity in CTP source images at dose reductions up to 90% (SSIM 0.81, PSNR 43 dB). Perfusion maps showed slightly lower similarity. Clinically, denoising substantially improved the accuracy of CBF-derived infarct core volumes, reducing mean absolute error from 10-30 mL in noisy images to approximately 4-16 mL and restoring high agreement with reference volumes (R2 > 0.85). These findings demonstrate that substantial simulated radiation dose reductions can be compensated by the DAE while preserving clinically meaningful perfusion-derived biomarkers.

Retinal image quality significantly affects the performance of diagnostic artificial intelligence (AI) models and is typically improved with pupil dilation in clinical settings. However, in real-world settings where dilation is not feasible, suboptimal image quality remains a challenge for AI deployment. In this study, we fine-tuned a CofeNet model to enhance the quality of undilated retinal images. We performed pixel-level alignment and fine-tuned a generative CofeNet model using 313 paired and spatially aligned undilated and dilated retinal images. Model performance was evaluated on an internal test (120 image pairs) and two external tests. Image similarity was assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Image gradability was evaluated for undilated and model-enhanced images by three graders on a three-point scale (gradable, force gradable, ungradable), with consensus-based grading. Inter-rater agreement among the three graders was evaluated using Fleiss' kappa (k). In the internal test set, PSNR and SSIM improved significantly from 23.2 and 0.823 (undilated) to 28.7 and 0.923 (model-enhanced) when compared to ground truth dilated images (both p&#x2009;<&#x2009;0.001). The gradability of the images improved from 41% (49 of 120 images) to 76% after enhancement, although the inter-rater agreement decreased (overall Fleiss' &#x3ba; from 0.397 to 0.234). Similar trends were observed in the external tests. The enhanced images demonstrated improved quality and greater structural similarity to dilated images, suggesting the potential of the CofeNet model as an alternative approach to enhance image quality. Further validation is necessary to determine its clinical utility.

Polyimide (PI) exhibits outstanding thermal stability and mechanical rigidity; however, their inherently rigid backbones lead to intrinsic brittleness, poor fracture toughness, and inferior impact resistance. Conversely, polyurea (PUA) features excellent elasticity, tunable soft-hard segment architectures, and a favorable balance of tensile strength and elongation at break. Herein, we systematically investigate the thermal and mechanical properties of 12 distinct PI, PUA, and PI-PUA copolymer systems via all-atom molecular dynamics simulations. Simulations demonstrate that rigid aromatic moieties significantly increase Tg and elastic modulus, while flexible hexamethylene diisocyanate (HDI) yields the highest elastic modulus via dense hydrogen-bond networks despite lowering Tg. Fluorine substitution effectively increases fractional free volume and moderately reduces Tg. Toughness is evaluated by K/G. System L with bulky phthalide side groups exhibits the highest K/G of 3.24, suggesting potential for improved plastic deformability as a preliminary screening indicator. In contrast, HDI-containing systems E and H show the lowest K/G ratios, as strong interchain hydrogen bonding severely restricts segmental slippage and induces brittle fracture. PI-PUA copolymerization proves to be an effective strategy to balance stiffness and toughness over a broad performance range. This work establishes structure-property correlations for PI-PUA systems, offering molecular-level insights for the rational design of advanced high-performance polymers, which require further experimental validation.

Individuals who wear Ankle Foot Orthoses (AFOs) express a desire for adaptability and improved stair ambulation. This exploratory study examined how the stiffness and neutral angle of a robotic AFO influence stair ambulation and preference in spastic cerebral palsy (CP) and compared robotic AFOs, solid AFOs, and shoes alone. Seven individuals with spastic CP completed modified Timed-Up-and-Down-Stairs (TUDS) tests under nine robotic AFO stiffness-neutral angle configurations, solid AFOs, and shoes alone. Outcomes included TUDS time, ankle kinematics, soleus and vastus lateralis activity, exertion, balance confidence, trunk instability, stiffness preference, and device power and torque. Mixed effects modeling assessed the effects of stiffness and neutral angle. Stiffness preference was evaluated during level walking, stair ascent, and descent. Repeated measures ANOVA compared robotic AFOs, solid AFOs, and shoes alone. Increased stiffness and more plantarflexed neutral angles decreased peak dorsiflexion (p&#x2009;<&#x2009;0.05) and increased peak torque (p&#x2009;<&#x2009;0.05). Few other outcomes were affected. Four of seven participants preferred low stiffness across all tasks. Robotic AFOs performed better than solid AFOs, by decreasing TUDs time (-&#x2009;27%, d&#x2009;=&#x2009;-&#x2009;0.66, p&#x2009;<&#x2009;0.05) and increasing confidence (49%, d&#x2009;=&#x2009;0.92, p&#x2009;<&#x2009;0.05) in descent, but not better than shoes alone. During stair ascent, the only difference between the AFOs and shoes was a reduction in soleus activity between solid AFOs and shoes alone. Robotic AFO configuration influences stair descent and may improve descent speed and confidence over solid AFOs in a limited sample of spastic CP.

Augmented World Expo (AWE) USA 2026 took place from June 15 to 18 in Long Beach, California, and brought together researchers, developers, investors, start-ups, and tech experts from across the extended reality (XR) ecosystem. In this News and Perspectives article, JMIR Correspondent Jos&#xe9; Ferrer Costa reports on the major themes and trends he observed at the conference.

Key message: In a large multinational validation study, the Kawasaki MATCH machine-learning clinical decision support tool accurately identified patients with Kawasaki Disease (KD) using data from the REKAMLATINA network. This is the first international validation of Kawasaki MATCH. Prospective and retrospective validation of the model across numerous diverse Latin American clinical settings demonstrates consistent performance despite differences in laboratory availability, data completeness, and practice patterns. These findings support the use of AI-assisted decision support to improve recognition of KD, reduce diagnostic delay, and potentially prevent coronary artery complications in children across varied health systems.

Ganoderma lucidum polysaccharide (GLP) exhibits prominent antibacterial and antioxidant activities. This study evaluated the effects of GLP seed soaking at two concentrations (4 g/100 kg, GLP4; 8 g/100 kg, GLP8) on wheat growth promotion and induced resistance against wheat sharp eyespot caused by Rhizoctonia solani and Fusarium head blight (FHB) mainly caused by Fusarium graminearum. Physiological and agronomic analyses showed that GLP treatment increased the germination rate of all tested wheat cultivars (moderately resistant: Xiaoyan 22, Sumai 3; moderately susceptible: Mingxian 169, Xinong 873) by over 2%. Moderately susceptible and resistant cultivars presented average plant height increases of 0.5 cm and 2 cm, respectively, with most cultivars showing a height increase of approximately 3 cm. GLP significantly elevated leaf chlorophyll content by over 5% and differentially regulated malondialdehyde (MDA) levels: MDA decreased by 34-68% in Sumai 3 and Mingxian 169 but increased by 11-23% in Xiaoyan 22. Pot assays verified that GLP yielded over 10% control efficacy against both diseases. Overall, GLP seed soaking effectively promotes wheat growth, activates defense responses, and enhances host resistance to R. solani and F. graminearum infections.

The endoplasmic reticulum (ER) hosts several integral membrane enzymes responsible for post-translational modifications of proteins entering the secretory pathway. These include protein glycosylation and the attachment of glycosylphosphatidylinositol (GPI) anchors. At the ER membrane, protein glycosylation is catalyzed by glycosyltransferases from the C-superfamily (GT-C), which use lipid donor substrates to attach a complex oligosaccharide to asparagine residues (N-glycosylation), or a single mannose unit to threonine, serine (O-mannosylation), or tryptophan (C-mannosylation) residues. In contrast, the attachment of GPI anchors to acceptor proteins is catalyzed by the multimeric enzyme transamidase, which cleaves a C-terminal GPI signal peptide of the acceptor protein and replaces it with a GPI anchor. In the present review, we will discuss recent mechanistic studies that shed light on the architecture of these membrane protein machineries and on how they recognize their substrates and catalyze protein glycan modifications at the ER membrane.

Aerobic granular sludge (AGS) represents a promising biological treatment technology for antibiotic-containing wastewater. However, most previous studies have focused on low-concentration antibiotic stress (&#x3bc;g/L to mg/L), while the long-term response of AGS to ultra-high oxytetracycline (OTC) levels is poorly understood. In this study, a laboratory-scale sequencing batch reactor was established to systematically investigate the AGS granulation, pollutant removal performance, and microbial community succession under long-term stress of 50&#xa0;mg/L OTC, a concentration representative of pharmaceutical effluent. The results showed that, despite the severe inhibitory effects of OTC, AGS was successfully cultivated within 28&#xa0;days, and its granulation period was comparable to that in conventional municipal wastewater without antibiotic stress. Furthermore, AGS exhibited good settling performance and stable biological activity. Pollutant removal gradually stabilized with granule maturation, resulting in removal efficiencies of 75% for chemical oxygen demand, 32% for ammonium nitrogen, 40% for total nitrogen, 54% for total phosphorus, and 56% for OTC, respectively. OTC removal gradually shifted from adsorption-dominated process in the start-up stage to a synergistic adsorption-biodegradation process in the stable stage. Spearman correlation analysis further indicated that the recovery of pollutant removal efficiency was more closely associated with microbial functional adaptation than with biomass accumulation alone. Microbial analysis revealed that Saprochaete, Bordetella, and Raoultella gradually became dominant, while carbohydrate metabolism and amino acid metabolism were significantly upregulated to mitigate OTC stress. These synergistic interactions between dominant genera and metabolic functions jointly constituted the core microbial mechanism for maintaining structural stability and pollutant removal performance of AGS under high OTC stress.

Heart failure with reduced ejection fraction (HFrEF) is characterized by impaired cardiac contractility. AC01, a small-molecule ghrelin receptor agonist, enhances contractility in cardiomyocytes. This study evaluated the in vivo hemodynamic effects of AC01 in a mouse HFrEF model and in cynomolgus monkeys. In HFrEF mice, intravenous AC01 significantly increased cardiac output, stroke volume, and ejection fraction versus vehicle, without any apparent detriment to diastolic function. Pressure-volume loop analysis demonstrated load-independent inotropic effects. In monkeys, oral AC01 increased cardiac output and stroke volume while reducing heart rate, without lowering central aortic pressure. These effects were sustained over 14 days of oral dosing. Additionally, AC01 improved autonomic balance by increasing parasympathetic and decreasing sympathetic activity. Overall, AC01 produced rapid, consistent, and sustained improvements in systolic function across species, supporting its potential as a novel, load-independent inotropic therapy for heart failure.

Bisphenol A (BPA) is a potential risk factor for pancreatic ductal adenocarcinoma (PDAC). This study integrated network toxicology, molecular docking, molecular dynamics (MD) simulations, and TCGA clinical data analysis to explore the potential molecular mechanisms linking BPA exposure to PDAC risk. BPA-PDAC intersection targets were identified through multi-database screening, followed by protein-protein interaction (PPI) network construction to screen core hub genes. A total of 10 core hub genes were identified via PPI analysis combined with the Maximal Clique Centrality (MCC) algorithm. Molecular docking demonstrated that ESR1 exhibited one of the strongest binding affinities for BPA (-8.2 kcal/mol), and MD simulations confirmed favorable thermodynamic stability of the BPA-ESR1 complex. TCGA analysis revealed stage-dependent expression patterns: early stages showed downregulation of TP53 and BCL2, whereas advanced stages showed upregulation of BCL2L1, HSP90AA1, and HSP90AB1, while ESR1, HIF1A, and PARP1 remained consistently low. These findings suggest that BPA may promote PDAC progression by disrupting ER&#x3b1;-mediated endocrine signaling and impairing DNA repair through PARP1 interference, providing candidate molecular targets and a hypothesis-generating foundation for pancreatic cancer risk assessment, warranting further experimental validation.

Geographical traceability of rice is critical for authenticity identification and quality control, yet it poses considerable challenges for tracing origins in adjacent small-scale producing areas. To explore the causes of metabolic differences and geographical traceability potential of rice from adjacent small-scale producing areas, non-targeted metabolomics combined with multivariate statistical analysis was employed to systematically investigate the metabolic profiles of rice from Panjin (PJ), Donggang (DG) and Yingkou (YK) in Liaoning Province. The characteristic metabolic markers for each producing area were screened, and the effects of climatic and soil factors as well as their interactive effects on grain metabolite composition were elucidated. The results showed that the partial least squares-discriminant analysis (PLS-DA) model established based on differential metabolites achieved acceptable discrimination among rice samples from the three regions. With variable importance in projection (VIP) > 2.0 as the screening threshold, the core characteristic markers of each producing area were determined: PJ is LPC 17:2, LPA 18:3, D-(+)-Arabitol, DG is 2'-Deoxyadenosine, LDGTS 18:2, and YK is LPE 17:2; the markers are mainly primary metabolites, including lipids, sugar alcohols and nucleotides. Sunshine duration, air humidity, wind conditions and soil layer temperature were highly correlated climatic drivers responsible for metabolic differentiation, and characteristic metabolic markers from different producing areas exhibited distinct meteorological response patterns. Soil physicochemical properties and mineral elements significantly affected the differential accumulation of metabolites, among which soil Sr element and organic matter exhibited crucial indicative significance for metabolic variation of rice in adjacent regions. Multi-factor interaction analysis verified significant synergistic coupling effects between regional climate and soil environment. Meteorological factors, including sunshine, wind and soil temperature, together with soil chemical factors involving organic matter, pH, Sr, K and Ca, were identified as core driving factors for the spatial differentiation of region-specific rice metabolites. The present study provides theoretical support at the metabolic level for the construction of a small-scale rice geographical traceability system and the mechanism research on the regional quality formation of rice.