搜索结果：Journal of environmental health science & engineering

The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI. A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. Gates Foundation, Queensland Health, and University of Queensland.

To assess the ability of former professional association football players in England to recall their playing careers. Self-reported data regarding playing careers were available from former professional football players participating in the Health and Ageing Data IN the Game of football (HEADING) study. We compared the self-reported data from 141 participants regarding the teams, positions, periods, and leagues they played for against the data available in publicly available records, compiled with the assistance of the English Professional Footballers' Association. Self-reporting of career histories occurred a mean of 28.5 years since the players had their last competitive season. Overall, 20 discrepancies between self-reported and register-based data were observed in the career histories of 18 (12.8%) individual players. This is in line with data from earlier reliability studies on the self-reporting of occupational histories. The most common error observed was incorrect recording of years of play in certain clubs, occurring in 9 instances. Missing teams in self-reports were observed in another 8 cases. The results suggest that former professional association football players can provide plausible information over long periods regarding the general characteristics of their professional career. This information could form the basis for a reliable exposure assessment within epidemiological analyses. Further work is required to assess recall of the amount of heading and other head impacts during training and play.

Tree decline can significantly impact both the aesthetic value and ecological function of urban forests. Understanding the abiotic factors influencing tree health is essential for developing effective management strategies. This study investigated the decline of heritage Araucaria heterophylla (Norfolk Island Pine) in urban areas of the Gold Coast, Queensland, Australia, with a focus on identifying underlying abiotic causes. General assessment was conducted along a 5-km coastal strip (including 946 trees); climate conditions on the Gold Coast and Norfolk Island were compared; and 38 selected trees of varying health status were examined in detail. This included soil physicochemical and nutrient analysis, as well as foliar nutrient profiling. Results showed that 15.8% of the 946 initially assessed trees exhibited symptoms of decline, such as dieback, browning of foliage tips, extensive defoliation and shortened foliage length. Mean temperatures on the Gold Coast were consistently higher than those in the species' native range and the current decline followed a period of elevated temperatures and reduced rainfall. Declining trees (n = 32) from the subset population were found to be growing in compacted soils, with significantly elevated foliar sodium and lower foliar carbon levels compared to healthy trees (n = 6), indicating possible disruption of nutrient regulation. Half of the trees in the advanced stage of decline were located less than 5 m away from carparks (50%), which was higher when compared with Moderate Decline (18.7%) and very healthy Benchmarking trees (0%). Most trees in the advanced decline category were not mulched and had turfgrass as the primary groundcover (62.5%), whereas trees in moderate decline were more commonly associated with turfgrass combined with fallen branchlets (43.75%). In addition, all Benchmarking trees were located in sandy soils, whereas declining trees were more commonly associated with loam soils, which are more susceptible to compaction. Key abiotic stressors identified were extreme weather and proximity to urban infrastructure. Management recommendations include improving root zone conditions by providing irrigation during dry periods, mulching, implementing routine health monitoring to support early intervention and, for new planting, increasing distances from hard infrastructure.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with frequent exacerbations of COPD (ECOPD) significantly impacting patient health and health care systems. Predicting ECOPD early would increase patients' quality of life and decrease the economic burden. The advancement of wearable technologies and Internet of Things (IoT) sensors has enabled continuous remote monitoring (RM), offering new opportunities for early ECOPD prediction. However, effectively leveraging wearable data requires robust artificial intelligence (AI) frameworks capable of processing heterogeneous physiological and environmental information. This systematic review aims to provide a comprehensive overview of both hardware and software solutions for predicting ECOPD using RM. From the reviewed literature, we first focus on key physiological and environmental variables essential for COPD monitoring that can be extracted from wearables and IoT sensors. Second, we describe the wearable and IoT devices currently deployed in COPD management. Finally, we review machine learning, including deep learning models, used for ECOPD prediction, discussing limitations for real-world implementation. By bridging AI-driven data processing with real-world sensor applications, this review aims to outline the current landscape, existing challenges, and future directions for developing effective RM solutions for ECOPD predictions. A comprehensive search was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies using AI or machine learning techniques for predicting ECOPD in in-home contexts. This review identified 26 studies that met the inclusion criteria. Twenty studies aimed at predicting or detecting exacerbations at the onset. The variables tracked most frequently were heart rate (n=9), peripheral oxygen saturation (n=9), and symptoms (n=8). Daily or weekly sampling was most common (n=14). Most studies (n=13) applied machine learning models-primarily random forest (n=5), CatBoost (n=2), decision trees (n=2), and support vector machines (n=2). Deep learning was used in 3 papers, while the remaining applied rule-based logics and probabilistic models. Wearables and IoT were used in only 6 out of 20 studies. Six papers analyzed changes in vital parameters during prodromal phases, defined as the period shortly before the onset of an exacerbation. Three studies collected data continuously, 2 daily, and 1 compared once-daily versus overnight monitoring; 4 of these 6 used wearable devices. Overall, current evidence highlights the potential of continuous monitoring of physiological and environmental variables for early ECOPD prediction, offering advantages over questionnaires or once-daily measurements. While wearables and IoT devices show promise, their use remains limited. Many studies rely on balanced datasets that do not mirror real-world exacerbation patterns and lack external validation across diverse populations. Future research should emphasize large-scale validation, integration of multimodal data, and translation of AI models into clinically feasible tools to enable timely intervention and improve COPD management.

As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High energy consumption, electronic waste, and carbon emissions associated with the deployment and training of AI models pose sustainability challenges that must be addressed. This paper investigates the concept and application of Green AI in healthcare, aiming to balance technological advancement with environmental responsibility. This systematic review explores key themes, including green computing practices, the adoption of energy-efficient AI models, and the use of renewable energy sources within healthcare settings. It identifies a range of healthcare applications employing Green AI, highlights emerging trends, and emphasizes the growing importance of environmental awareness in AI development. Furthermore, the study examines enabling tools and techniques, outlines barriers to adoption, and highlights how Green AI can help streamline processes, reduce resource waste, and promote environmentally friendly medical procedures like telemedicine. The review also discusses the significance of policy frameworks, international initiatives, and cross-sector collaboration in promoting environmentally responsible AI deployment. Finally, the paper presents practical implications and outlines future research directions to guide the sustainable evolution of AI in healthcare.

Although recent studies have suggested an association between air pollution and Kawasaki disease (KD), evidence regarding prenatal exposure and subsequent KD risk in children remains limited. This study aimed to evaluate the association between prenatal air pollution exposure and KD incidence in children. We used the Big CHildren's ENvironmental health Study covering mother-child pairs based on the National Health Information Database. We defined KD onset using the tenth revision of the International Classification of Diseases (M30.3) and immunoglobulin prescriptions. We used a multivariate Cox proportional hazards model to evaluate the association between prenatal air pollution exposure (fine particulate matter [PM2.5], particulate matter [PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) and KD in children. The model was adjusted for maternal age, child sex, income level, maternal occupation, birth season, birth year, and region. Hazard ratios (HRs) and 95% confidence intervals (CIs) were evaluated per interquartile range increase in exposure to PM2.5, PM10, SO2, NO2, and O3. We analyzed 1624,230 mother-child pairs and identified KD onset in 13,126 children (0.8%). Prenatal exposure to PM10, SO2, and NO2 during the second and third trimesters, as well as across the entire pregnancy, was associated with an increased risk of KD, with the strongest associations observed during the third trimester (PM2.5: 1.046, 95% CI: 1.007-1.087; PM10: 1.104, 95% CI: 1.061-1.149; SO2: 1.052, 95% CI:1.117-1.153; NO2: 1.117, 95% CI: 1.082-1.153). We found that exposure to air pollution during pregnancy was positively associated with KD risk in children.

Rapid, sensitive and selective H2O2 and dopamine sensors provide enormous opportunities to health, food and environmental monitoring, which could prevent major social and economic losses. To overcome the sluggish response and low sensitivity of the conventional colorimetric assays, an electrochemical platform integrated with the conventional assay was proposed in this work. First, a microwave-assisted cobalt MOF (Co-MOF) was synthesized using a bio-linker and characterized using FESEM and TEM. The electrochemical performance of Co-MOF was examined through cyclic voltammetry (CV), where eight-fold higher currents were achieved for Co-MOF compared to those of the unmodified electrode. However, Co-MOF exhibits very weak nanozymatic activity in a mixture of 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2. Integration of the superior electrochemical characteristics of Co-MOF with the nanozymatic activity resulted in a six-fold enhanced nanozymatic activity that enabled H2O2 quantification with a limit of detection (LOD) of 32 nM under optimized conditions. The modified electrode was further used to quantify dopamine, achieving an LOD of 0.81 µM, with a remarkably shorter detection time (60 fold shorter) compared to the conventional nanozyme. A mechanistic study showed that Co-MOF provides a large surface area and abundant redox-active sites, facilitating fast electron transfer and significantly enhancing the electrochemical signal.

Does swidden agriculture, a prototypical coupled human and natural system, exhibit a process of adaptive self-organization in which cultural practices balance environmental constraints through adaptive feedback? Here, we investigate whether quantitative signatures of adaptive self-organization can be detected in a dataset consisting of 18,000+ contiguous swidden patches in 18 remote sensing images of swidden mosaics from tropical and subtropical regions globally. We find that the distributions of patch sizes in 16 of 18 swidden areas exhibit power law patterns with scaling exponents ≈1, and correlation distances of ≈548 m. To account for these patterns, we develop a plausible ethnographically informed agent-based model of labor exchange, land use, and swidden site selection in which both sustainable and unsustainable resource uses can emerge out of interactions among individuals or households. By analyzing the model, we identify spatial synchronization of swidden sites as the driver of power law formation, while social norms of swidden labor can guide the system to an intermediate level of landscape disturbance. Both mechanisms are required to maintain harvests and ecosystem productivity at high levels. Our model advances theoretical understanding of the socioecological dynamics of swidden agriculture, and supports the hypothesis that adaptive self-organization may be a general characteristic of coupled human and natural systems.

Accurate self-reported data on social determinants of health (SDoH) are essential for improving prevention initiatives. Beyond survey content and validation, deciding which household member should complete family-level SDoH assessments can affect data quality. Yet, few studies have explored how family members report on various SDoH, especially in the Latino/Hispanic community experiencing greater health challenges. This study not only examined intra-household agreement on family-level SDoH items but also assessed combined individual SDoH linked to agreement in Southern California, which hosts one of the largest Latino/Hispanic communities in the USA. We analyzed data from 277 adult pairs (n = 554) across Southern California who completed the National Institute on Minority Health and Health Disparities Common Data Elements questionnaire. Each respondent answered 19 household-level items across four domains: Demographics, Economics, Health and Clinical Care, and Housing. Agreement was evaluated using simple or weighted Cohen's Kappa (range, ≤ 0 to 1). Modified Poisson regression examined associations between agreement and combined individuals' health literacy, employment, sex at birth, age, birthplace, and ethnicity. Tests were corrected for multiple comparisons. Among respondents, 56.3% were women, participants' mean age was 41.8 years (standard deviation = 8.6), 93.1% identified as Latino/Hispanic, and 37% reported low health literacy. Agreement across domains varied (Kappa = 0.14-0.85), with higher agreement observed for Demographics and transportation items, and poor agreement on financial adversity, healthcare, and food insecurity items. Agreement on food insecurity items varied by dyad composition: same-sex dyads demonstrated greater agreement than mixed-sex dyads, whereas dyads in which both members identified as Latino/Hispanic showed lower levels of agreement. Variations seem to exist in how household members report on family-level SDoH, particularly for sensitive areas like finances and food access. To improve accuracy, researchers and public health professionals might consider collecting data from multiple household members while encouraging joint responses and accounting for specific when designing assessments.

Water pollution from agricultural, industrial, and urban activities threatens aquatic ecosystems and the essential services they provide. Excess nitrogen has been identified as a key driver of water quality degradation, impacting biodiversity, food security, and human health. One approach to mitigating nitrogen pollution is water quality offsetting, compensating for pollution impacts by implementing nitrogen reduction measures elsewhere to achieve no net decline in water quality. Selecting and implementing suitable nitrogen offset types remains challenging. This study presents a framework for identifying, selecting, and implementing effective nitrogen offset strategies in tropical and subtropical regions globally, using the Great Barrier Reef Catchment Area (GBRCA) as an example. This framework is based on the premise that effective nitrogen offset design requires integrating evidence on nitrogen mitigation performance, appropriate criteria for selecting offset types, and adaptive management to address uncertainty in environmental outcomes. Accordingly, the framework comprises three interrelated components: (1) assessment of water quality improvement methods for nitrogen reduction in the GBRCA and other tropical and subtropical regions based on performance (i.e., efficacy, effectiveness, and efficiency); (2) selection of suitable offset types based on their performance, co-benefits, and spatial and technical feasibility; and (3) integration of adaptive management strategies, Geographic Information Systems (GIS) tools, and monitoring systems to strengthen the effectiveness of nitrogen offsetting. This is the first global assessment of nitrogen offset performance in tropical and subtropical regions, offering insights to improve water quality management through offsetting. This framework is adaptable to other regions and pollutants, guiding effective offset implementation to enhance watershed resilience.

Sulfonamides (SAs) are widely used in the livestock industry and for the prevention and control of human diseases. However, the residues of these antibiotics have the potential to pose significant threats to environmental safety and human health. To overcome the limitations of traditional detection methods, which are time-consuming, labor-intensive, and insufficiently sensitive, it is essential to establish an efficient detection method for SAs. This study developed a ratiometric fluorescent biosensor based on CDs/AuNCs@ZIF-8 material that integrates broad-spectrum aptamer and linear causal regulation for the broad-spectrum detection of SAs in milk, surface water, and chicken. The combination of broad-spectrum aptamer and cascade amplification technology improves detection accuracy. It also ensures highly specific, broad-spectrum detection of five typical SAs. At the material level, ZIF-8 encapsulation significantly enhances the fluorescence quantum yield of AuNCs. It also increases the composite's stability in complex matrices by restricting non-radiative transitions. CDs serve as a stable internal reference signal, overcoming the effect of environmental fluctuations on a single fluorescence signal. Under optimized conditions, the method achieves an ultralow detection limit of 1.86 pM for SAs. Its detection sensitivity significantly outperforms state-of-the-art analogous methods. The method offers simple operation and excellent matrix adaptability, making it an ideal technical approach for rapid screening of antibiotic in multi-matrix samples.

Mercury ion, a highly toxic and bioaccumulative heavy metal pollutant, poses significant risks to human health and ecosystems even at trace concentrations. Therefore, the development of highly sensitive and selective analytical methods for mercury ions is critically important to safeguard environmental integrity and human health. In this work, 4-mercaptopyridine-functionalized gold nanoparticles (4-MPY-AuNPs) were synthesized and subsequently immobilized onto quartz slides to fabricate a localized surface plasmon resonance (LSPR) sensor. Exploiting the selective coordination interaction between the pyridyl nitrogen atoms of 4-MPY and Hg2+, this LSPR sensor enables highly specific detection of Hg2+. Moreover, injecting a trace amount of 4-mercaptopyridine-functionalized AuNPs into the flow cell triggers the in situ formation of a surface-confined AuNP-Hg2+-AuNP sandwich architecture, thereby enhancing the sensor's sensitivity. Under the optimized conditions, the proposed method exhibited a linear dynamic range of 1 × 10-9-6 × 10-7 mol L-1, with a correlation coefficient (R2) of 0.9917 and a limit of detection (LOD) of 3.2 × 10-10 mol L-1; the LOD of this method is one order of magnitude lower than the LODs reported in contemporary Hg2+ detection methods. This method exhibits high selectivity, sensitivity, cost-effectiveness, and is label-free, thereby demonstrating significant potential for environmental applications.

Glutathione (GSH) and hydrogen peroxide (H2O2) are both critical biomarkers for human health. GSH serves as a key non-protein thiol that maintains cellular redox homeostasis, and its abnormal levels are closely associated with neurodegenerative diseases. H2O2, as a potent oxidant, finds extensive applications in medicine, environmental science, and energy fields. In this study, a novel switchable colorimetric nanozyme (FeCo-MOF-on-ZIF-67) was developed via a MOF-on-MOF strategy, enabling ultrasensitive detection of GSH and H2O2. The detection process involves an initial step for GSH, followed by the complete oxidation of GSH by H2O2, which subsequently allows for H2O2 detection. During colorimetric detection, increasing concentrations of GSH lead to a decrease in absorbance, exhibiting a negative linear correlation. In contrast, increasing concentrations of H2O2 result in an increase in absorbance, showing a positive linear correlation. This switching mechanism is achieved through the specific reduction-induced quenching of the peroxidase-like activity of FeCo-MOF by GSH, followed by the competitive restoration of catalytic activity upon the introduction of H2O2. Experimental results demonstrate that the detection ranges for GSH and H2O2 are 1-200 nM and 2-200 nM, with detection limits as low as 0.74 nM and 1.72 nM, respectively-substantially outperforming conventional colorimetric methods. Real-sample analysis confirmed excellent biocompatibility and practical application potential of the sensing platform, with favorable recoveries and low RSD values for GSH in pond water, bovine serum and H2O2 in tap water.

Heavy metal contamination in food crops remains a critical environmental and public health issue, particularly for cadmium (Cd2+) and copper (Cu2+), which can accumulate through soil, water, and agricultural inputs. Reduced graphene oxide (rGO) was prepared via boric acid-assisted thermal reduction of graphene oxide followed by acid washing. A boron-doped reduced graphene oxide modified graphite electrode (rGO/GE) was then fabricated and applied for the sensitive voltammetric determination of Cd2+ and Cu2+ in jasmine rice using differential pulse anodic stripping voltammetry (DPASV) coupled with a standard addition technique. DPASV analysis included immersion of the rGO/GE and the deposition of target metal ions (Cd2+ and Cu2+) and bismuth onto the electrode surface. The synthesized GO and rGO were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), UV-vis spectroscopy, and scanning electron microscopy coupled with energy dispersive X-ray spectrometry (SEM-EDS). Results confirmed the complete reduction of GO to rGO using boric acid as the reducing agent. The method showed excellent reproducibility and sensitivity, with limits of detection (LODs) of 30 µg/L for Cd2⁺ and 0.05 µg/L for Cu2⁺. Electrochemical analysis results show that the actual concentrations of Cd2+ and Cu2+ in Royal Umbrella brand jasmine rice were 0.46 ± 0.01 mg/kg and 1.16 ± 0.06 mg/kg, respectively. The results were validated using atomic absorption spectroscopy (AAS), which provided relative differences of 0.00% for Cd2⁺ and 8.7% for Cu2⁺. These findings demonstrate that the rGO/GE sensor provides a reliable and cost-effective tool for trace heavy metal assessment in food matrices, offering significant potential for routine environmental and food safety monitoring. HIGHLIGHTS: An rGO/GE electrode was developed for sensitive detection of Cd2⁺ and Cu2⁺ in rice. Results of the method showed excellent agreement with AAS, with low relative differences (0.00% and 8.7%). The proposed electrochemical sensor offers a rapid, precise, and low-cost alternative for heavy metal monitoring in food samples.

Polycystic ovary syndrome (PCOS) is associated with an increased risk of neurodevelopmental disorders in offspring, yet how maternal PCOS interacts with environmental toxicants to influence fetal brain development remains unclear.We hypothesized that an AMH-programmed PCOS-like background increases neurodevelopmental vulnerability, which is worsened by gestational F-53B exposure through lipid metabolic reprogramming. Our study reveals how endocrine-metabolic dysfunction and environmental toxicants interact to impact fetal brain development. F0 dams were exposed to anti Müllerian hormone in late gestation to generate PCOS like F1 females and simultaneously received F-53B or vehicle, yielding four groups: Con, AMH, F-53B, and AF. F1 females from AMH lineages exhibited reproductive abnormalities characteristic of PCOS, which were most pronounced in the AF group. Bulk RNA sequencing of E14.5 F2 embryonic brains revealed progressive transcriptomic divergence across groups, with AF embryos showing the greatest shift from controls. Genes differentially expressed in both the F-53B vs Con and AF vs AMH comparisons were enriched in lipid metabolism and PPAR-related pathways, accompanied by graded upregulation of Cidec, Plin1, Fabp4, and Pparg and reciprocal downregulation of Aqp7, which was confirmed at the protein level for CIDEC, PLIN1, and AQP7. At the cellular level, AF embryos exhibited the most severe neurodevelopmental defects, including loss of TBR2⁺ positive intermediate progenitors, reduced TBR1⁺ and SATB2⁺ cortical neurons, diminished Neurod1⁺ and Tuj1⁺ expression, and decreased Olig2⁺ positive oligodendroglial cells. We hypothesized that an AMH-programmed PCOS-like background would prime offspring for subtle neurodevelopmental vulnerability, F-53B exposure during pregnancy would induce lipid metabolic reprogramming in the offspring fetal brain, and the combination of AMH-induced PCOS-like programming and F-53B exposure would exert 'two-hit' effects, leading to the greatest disruption of neurogenesis and gliogenesis. By linking an emerging PFAS alternative to mechanistically grounded alterations in fetal brain lipid metabolism and neural lineage development on a PCOS-like background, our work provides an integrated framework for understanding how endocrine-metabolic disorders and environmental contaminants converge to shape neurodevelopmental risk.

The number of published isothermal amplification assays has increased substantially in recent years. Unfortunately, no harmonized guidelines, such as the Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines, exist for publishing these methods, often resulting in incomplete reporting of assay composition and performance. In this study, we systematically evaluated nine published loop-mediated isothermal amplification (LAMP) assays for the detection of Pseudomonas aeruginosa. We aimed to assess whether publications provide (i) sufficient information on assay composition to allow implementation and reproduction and (ii) robust data on assay performance to evaluate their applicability. Assays were screened for basic functionality, analytical specificity, sensitivity, and limit of detection (LOD) in head-to-head experiments with qPCR. Four assays lacked essential composition details, and almost all did not report DNA concentrations or replicate numbers. Only six assays consistently amplified target DNA. Analytical specificity testing with 19 non-target strains contradicted previously reported 100% specificity, with only 3 maintaining specificity above 90% in our evaluation. Sensitivity testing with 13 P. aeruginosa strains confirmed 100% sensitivity for two assays. However, LOD experiments revealed significantly higher values than originally reported, with qPCR outperforming all LAMP assays. These findings highlight substantial discrepancies between published data and real-world assay performance. The absence of standardized formats, consistent units, and complete methodological details undermines replicability. Although this study focused on P. aeruginosa, the identified issues are widely relevant across different microbial targets. We advocate for increased publication standards and quality controls to ensure transparency, utility, and comparability of isothermal amplification assays and to support their translation into clinical and environmental applications.IMPORTANCEOver the past decade, numerous isothermal amplification-based assays for the detection of pathogens or health-relevant microorganisms have been proposed, each claiming progress from the state-of-the-art and applicability to both clinical specimens and/or environmental samples. However, many published assays lack essential methodological details, and reported performance metrics are often inconsistent or incomparable. Using Pseudomonas aeruginosa as a representative model organism, we set out to test whether all details required for implementing assays were provided by original publications and if important assay characteristics were reproducible. The results of this systemic benchmarking of nine published loop-mediated isothermal amplification assays revealed major discrepancies between reported and experimentally measured performance in terms of specificity, sensitivity, and limit of detection. By highlighting the crucial elements that need to be reported, this work aims to improve the transparency, reproducibility, and overall quality of isothermal amplification assays, fostering their broader application across different research settings.

The pace of surgical innovation appears ever faster. Innovation is being freed from the design constraints of the opposable digits of a surgeon's hand through the use of programmable binary digits. Surgeons must be the drivers of change and central to the application of innovations. We should collaborate with industry, engineers and scientists to think out of the box but must consider also expense, environmental impact, equity, and ethics. But we should not be blinded by shiny technology: innovation without impact is mere noise. The ultimate considerations are the diagnosis and management of surgical disease, of improving the care of our patients. Expert surgeons, scientists and engineers across the world were identified and invited to describe areas of innovation within surgery. They were given free rein to review their areas of expertise and to discuss both current and future applications of technology within surgical care. The Commission spans multiple surgical specialties and scientific domains. It reviews translational genomics, including the role of ctDNA, alongside microbiomic and proteomic applications in improving the diagnosis, treatment and monitoring of surgical disease. Applications to enhance surgical procedures are described, from medical micro/nanorobots for minimally invasive interventions, sensory-enriched surgery with visual optimization and molecular image-guidance to intelligent and semiautomated instruments. The expansion and broad influence of artificial intelligence in surgical writing, training and simulation, diagnosis and robotics is widely described. The role of surgical innovation and technology in driving personalized care for benign and malignant surgical disease from genomic profiling to bespoke surgical and non-surgical treatment pathways and surveillance is considered. The future of surgery is poised to become more precise, personalized, and effective. Collaboration with engineers, data scientists, and industry partners not only represents an exciting opportunity for surgeons to participate in team science but is critical to focus innovation goals on optimizing patient care and outcomes.

The development of sensitive and selective sensing platforms for monitoring antibiotic residues is crucial for environmental protection and public health. Herein, a highly luminescent terbium-based metal-organic framework (Tb-MOF) was synthesized using a dual-ligand strategy with 1,4-benzenedicarboxylic acid (H₂bdc) and 1, 2, 4-benzenetricarboxylic acid (H₃btc). By encapsulating Rhodamine B (RhB) into its pores, a novel ratiometric fluorescent probe, RhB@Tb-MOF, was fabricated. This probe demonstrates selective and sensitive turn-on fluorescence response toward norfloxacin (NOR) and turn-off responses toward tetracycline (TC) and nitrofurazone (NFZ), achieving notably low detection limits of 9 nM, 0.055 μM, and 0.125 μM, respectively. The underlying mechanisms for fluorescence enhancement and quenching were systematically elucidated, involving synergistic effects of the Antenna Effect, competitive absorption, photoinduced electron transfer, and intermolecular interactions. Furthermore, to address the challenge of discriminating these antibiotics in mixtures, a sensor array coupled with linear discriminant analysis (LDA) was developed, enabling high-precision classification of single, binary, and ternary antibiotic solutions. For practical application, a smartphone-enabled portable device was built for visual on-site quantitative detection. This work provides a reliable method for constructing high-performance fluorescent sensors in environmental monitoring.

Imidacloprid (IMI) has potential hazards to human health and honeybees. Herein, we developed a highly sensitive fluorescence polarization immunoassay (FPIA) based on ZIF-8-derived carbon particles (ZIF-8-C) for the detection of IMI in agricultural samples, and studied the effect of different sizes of ZIF-8-derived carbon-antibody conjugates (ZIF-8-C-PDA-mAb) and fluorescence lifetime on the detection sensitivity. The results found that the detection sensitivity of FPIA was related to the particle sizes of ZIF-8-C-PDA-mAb. Under optimized conditions, 220 nm ZIF-8-C-PDA-mAb-based FPIA showed the best limit of detection of 0.46 μg/L, which was 47-fold lower than that of the traditional mAb-based FPIA. The recoveries ranged from 86.75 to 111.75%, with a relative standard deviation below 8.44%. Additionally, FPIA results correlated well with HPLC, with a correlation coefficient of 0.998. This strategy provides a great potential FPIA method for rapid and high-sensitivity detection of pesticides and other small molecule pollutants in environmental and agricultural samples.