The misuse of the broad-spectrum fungicide imazalil (IMZ) poses significant challenges to food safety and environmental safety, which requires rapid and accurate methods for detecting IMZ in complex food matrix and environmental samples. Herein, a novel anti-IMZ nanobodies (Nbs) with excellent stability and high expression yield was from a high-efficiency capacity of over 1014 pfu/mL alpaca phage display nanobody library. Furthermore, the molecular recognition mechanism of anti-IMZ Nbs was simulated, and it was found that the causes affecting antibody sensitivity were mainly attributed to the hydrophobicity scales of active binding pocket, with synergistic enhancement of hydrogen bonding forces, and then proposed a directed evolution strategy for optimizing the anti-hydrophobic small molecule antibodies. Subsequently, a nanobody-based nanogold immunochromatography assay (Nb-GICA) was developed for IMZ detection in fruits and environmental water samples. The Nb-GICA achieved rapid detection (8 min per test), with a broad linear range (8.87–380.57 ng/mL) and a limit of detection (LOD) of 6.05–16.56 ng/mL. Additionally, the average spiked recovery rates in samples ranged from 82.6% to 117.5%, showing good consistency with GC-MS/MS results. The results demonstrated that the developed nanobody was a promising recognition element for the development of immunological methods to monitor IMZ residues in food and environmental samples.

Small extracellular vesicles (sEVs) membrane protein profile (sEVpp) is a novel biomarker for cancer, and it can reveal the in-depth phenotype information. The point-of-care testing (POCT) of sEVpp holds great significance for mass screening of cancer, so the cost-effective and simple detection methods of sEVpp are urgently demanded. Herein, we constructed a paper-based multichannel sEVpp POCT device (sEVpp-PAD) enabled by functional DNA probes and metal-organic framework (MOF). The core components are aptamer/MOF-modified paper chips. The modified aptamers can immunocapture the sEV expressing corresponding proteins, while the modified MOF can provide abundant sites for aptamer-modification, reduce the nonspecific protein absorption, and act as reference for ratiometric detection. Simply powered by two syringes, the sEVpp-PAD can efficiently capture sEVs expressing corresponding protein from cell culture media and sera. Furthermore, a detection probe (DP) consisted of CD63 aptamer and G-quadruplex was developed for the colorimetric detection of captured sEVs. Utilizing this device, the sEVpp in various hepatocellular carcinoma cell culture medium and, more importantly, in human sera can be accurately determined, only with $2 device, $0.2 detection reagents and 1.8 h procedure. This simple strategy for sEVpp detection can innovatively promote the POCT and subtyping of cancer based on sEV-related liquid biopsy. (c) 2025 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

The ongoing threat of emerging “Disease X”, exemplified by SARS-CoV-2, emphasizes the urgent need for non-invasive, rapid, and accurate diagnostic platforms suitable for home use. Here, we present EBCatch, an integrated system for the label-free electrochemical detection of respiratory viruses directly from exhaled breath condensate (EBC). The platform combines a semiconductor-based condenser for efficient EBC collection within 1 min, a carbon nanotube-based electrochemical biosensor functionalized with ACE2 receptors for specific virus recognition, and a smartphone application for automated result interpretation. Without the need for exogenous reagents or complex sample pretreatment, EBCatch achieves femtogram-level sensitivity (limit of detection = 1.6 fg/mL for pseudovirus, substantially below the viral load in EBC samples of infected individuals with SARS-CoV-2) and delivers results within 8 min from sample collection to readout. Clinical validation with total 155 samples demonstrated high diagnostic accuracy with sensitivity of 95.06%, specificity of 97.30% and overall accuracy of 96.13%, enabling detection in pre-symptomatic and antigen-negative stages. Notably, the EBCatch response reflects not only viral load but also viral activity, providing valuable insight into infectivity status and transmission risk. This versatile and user-friendly platform represents a significant advance toward decentralized, real-time monitoring of respiratory infections at home, enabling timely intervention during emerging outbreaks.

BackgroundThe infection of high-risk human papillomavirus (hr-HPV) and related cervical cancer have greatly threatened women's health. However, the benefits of ongoing strategies in China remains unclear. Insufficient vaccine supply and excessive screening workload have hindered the widespread implementation of HPV immunization plans.MethodsWe constructed stratified mathematical models to simulate the transmission of hr-HPV among women under ongoing public health strategies, and calculated the incremental cost-utility ratio (ICUR) to compare the health-economics benefits among different intervention pathways, including different vaccine type and dose schedules, commonly recommended screening algorithms as well as an artificial intelligence (AI)-assisted thin-layer cytology test (TCT) method. The model parameters were calibrated according to real-world HPV prevalences, incorporating segmented model assumptions reflecting the levels of COVID-19 lockdown.ResultsThe model shows that ongoing strategies in China are projected to reduce cervical cancer prevalence continuously and demonstrate cost-utility (ICUR: 27,592.62 USD/quality-adjusted life-year [QALY], 26,902.80-28,282.44) when increasing the participation rate to achieve the global goal by 2030. HPV vaccination provides substantial health benefits but cannot improve the cost-utility at current cost. Offering single dose of 2vHPV vaccine to girls before the age of 14 and reallocating excess doses to women under 25 yields a lower ICUR compared to two- or three-dose scenarios. Cervical screening can significantly reduce the ICUR. Among the screening methods, HPV testing demonstrates higher cost-utility, while AI-TCT outperforms all recommended traditional pathways.ConclusionsThe ongoing strategies demonstrate substantial health and economic benefits in achieving the 2030 global target; however, neither screening nor vaccination alone can deliver optimal effectiveness. The findings highlight the importance of combining vaccination and screening, and provide evidence for the promotion of single-dose vaccination and AI-TCT projects to alleviate resource burdens.

The approximately 40-week gestational period is central to human reproduction, yet the genetic architecture of diverse gestational phenotypes and their links to maternal late-life health remain unclear. In 111 phenotypes from up to 121,579 Chinese pregnancies (median n = 78,535 per phenotype), we identified 4,688 independent genome-wide significant signals, including 1,703 new associations. Gestation-specific effects were observed for 7.8% of variants across 30 phenotypes; 18.7% of signals for 24 longitudinal hematological traits exhibited genotype-by-gestational-timing interactions across five antenatal and postpartum periods. Dynamic genetic effects were enriched in growth-regulatory and hormone-regulatory pathways, reflecting maternal-fetal interactions. Genetic correlation and Mendelian randomization analyses with 80 diseases and medication traits in BioBank Japan females revealed shared genetic overlaps and potential causal links between gestational phenotypes and maternal mid-life and late-life health. These results establish a dynamic genetic atlas of human gestation, providing a framework for precision maternal health.

Small extracellular vesicles (sEV) are increasingly reported as biomarkers for the early diagnosis of pancreatic cancer (PC), but the current techniques for isolation and detection of sEV rely on expensive instruments and tedious protocols. In this work, a facile and rapid sEV isolation and detection method (LAPT-sEViso) was developed, which is based on the specific aggregation of GPC-1-positive PC-derived sEV and an aptamer-functionalized DNA long chain produced by rolling circle amplification (RCA-APT). The LAPT-sEViso can efficiently isolate sEV from cell culture medium and serum, showing 45 times higher yield (5.5 × 106 particles mL-1), 1.1 times higher purity (1.66 × 1010 particles mg-1) and 4.9 times higher recovery (80.9%) comparing to the traditional ultracentrifugation method, with only $1000 common instruments and $2.88 reagents/materials in 1 h. Moreover, after simple filtration and on-membrane ELISA, sEV concentration can be instrument-free detected with a limit of detection of 5.62 × 103 particles μL-1 (linear range from 5.0 × 103 to 5.0 × 107 μL-1). The LAPT-sEViso provides an efficient and practical approach for the rapid isolation and detection of sEV, providing a novel approach for the sEV-based liquid biopsy.

Small extracellular vesicles (sEV) are increasingly reported as biomarkers for the early diagnosis of pancreatic cancer (PC), but the current techniques for isolation and detection of sEV rely on expensive instruments and tedious protocols. In this work, a facile and rapid sEV isolation and detection method (LAPT-sEViso) was developed, which is based on the specific aggregation of GPC-1-positive PC-derived sEV and an aptamer-functionalized DNA long chain produced by rolling circle amplification (RCA-APT). The LAPT-sEViso can efficiently isolate sEV from cell culture medium and serum, showing 45 times higher yield (5.5 & times; 106 particles mL-1), 1.1 times higher purity (1.66 & times; 1010 particles mg-1) and 4.9 times higher recovery (80.9%) comparing to the traditional ultracentrifugation method, with only $1000 common instruments and $2.88 reagents/materials in 1 h. Moreover, after simple filtration and on-membrane ELISA, sEV concentration can be instrument-free detected with a limit of detection of 5.62 & times; 103 particles mu L-1 (linear range from 5.0 & times; 103 to 5.0 & times; 107 mu L-1). The LAPT-sEViso provides an efficient and practical approach for the rapid isolation and detection of sEV, providing a novel approach for the sEV-based liquid biopsy.

Gestational diabetes mellitus (GDM) affects ~14% of pregnancies and increases maternal type 2 diabetes mellitus (T2DM) risk. The GenDiP Consortium presents trans-generational, multi-ancestry genome-wide association study meta-analyses of GDM and pregnancy glycemic traits in up to 38,305 GDM cases and 776,145 controls. We identify 37 GDM-associated loci (7 novel) and five novel loci for pregnancy glycemic traits, all operating through the maternal genome. We classify 12 GDM variants with stronger effects in GDM than T2DM into five biologically informed categories, revealing pleiotropy patterns, pregnancy-dependent effect modification, and diagnostic heterogeneity. While all these loci overlap with T2DM and/or non-pregnant glycaemic traits, four (G6PC2, CAST-PCSK1, HKDC1, FOXA2) lack genome-wide-significant T2DM associations; GCK shows distinct causal variants for GDM, and MTNR1B exhibits pregnancy-amplified effects. Our findings provide new genetic insights into GDM and highlight the need for larger, ancestrally diverse studies of GDM and glycaemic traits during pregnancy to understand potential pregnancy-specific effects.

For a complex trait, heritability ([Formula: see text]) gives the genetic determination of its variation. Given the emergence of biobank-scale data, a more powerful method is needed to estimate [Formula: see text]. Based on the framework of Haseman-Elston regression (RHE-reg), we integrate a fast randomization algorithm to estimate [Formula: see text], and RHE-reg can tackle biobank-scale data, such as UK Biobank (UKB), very efficiently. Furthermore, we present an analytical solution that balances computational cost and precision of the estimation, a property that is important in dealing with biobank-scale data. We investigated the performance of the RHE-reg in simulated data and also applied it for 81 UKB quantitative traits; as tested in UKB data of nearly 300,000 unrelated individuals, it took on average about 4.5 hours to complete an estimation when used 10 CPUs. We extended the application of RHE-reg into distributed datasets when privacy is not compromised. As shown in UKB and simulated data the performance of RHE-reg was accurate in estimating [Formula: see text]. The software for estimating SNP-heritability for biobank-scale data is released.

High-quality genome databases derived from large-scale, family-based birth cohorts are vital resources for investigating the genetic determinants of early-life traits and the impact of early-life environments on the health of both parents and offspring. Here, we established a genomic platform for the Born in Guangzhou Cohort Study (BIGCS), the Genome Database of BIGCS (GDBIG), which represents the first birth cohort-based genomic database in China and is designed to facilitate intergenerational genetic research. Based on the phase I results of the BIGCS, GDBIG includes low-coverage (∼ 6.63×) whole-genome sequencing (WGS) data and extensive pregnancy phenotypes from 4053 Chinese participants. These participants are from 30 of China’s 34 provincial-level administrative divisions, encompassing Han and 12 minority ethnic groups. Currently, GDBIG provides a range of services, including allele frequency queries for 56.23 million variants across two generations, a genotype imputation server featuring a high-quality family-based reference panel, and a genome-wide association study (GWAS) meta-analysis interface for various maternal and infant phenotypes. The GDBIG database addresses the lack of Asian birth cohort-based genomic resources and provides a valuable platform for conducting genetic analysis, accessible online or via application programming interfaces at http://gdbig.bigcs.com.cn/.

Gestational diabetes mellitus, a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding its genetic architecture and its potential for early prediction using genetic data. Here we conducted genome-wide association studies on 116,144 Chinese pregnancies, leveraging their non-invasive prenatal test sequencing data and detailed prenatal records. We identified 13 novel loci for gestational diabetes mellitus and 111 for five glycemic traits, with minor allele frequencies of 0.01-0.5 and absolute effect sizes of 0.03-0.62. Approximately 50% of these loci were specific to gestational diabetes mellitus and gestational glycemic levels, distinct from type 2 diabetes and general glycemic levels in East Asians. A machine learning model integrating polygenic risk scores and prenatal records predicted gestational diabetes mellitus before 20 weeks of gestation, achieving an area under the receiver operating characteristic curve of 0.729 and an accuracy of 0.835. Shapley values highlighted polygenic risk scores as key contributors. This model offers a cost-effective strategy for early gestational diabetes mellitus prediction using clinical non-invasive prenatal test.

MircroRNA (miRNA) exhibits abnormal expression in many cancer diseases, and the detection and analysis of miRNA are significant for the early diagnosis of diseases and research on miRNA functions. In this work, we construct a UV-triggered DNAzyme (UTD) nanosensor for the early detection of miRNA in tumor cells. As the nanodevice was delivered into cells and irradiated by UV light, the controllable imaging of miRNA in living cells was achieved. This method effectively avoids false signal issues, providing a new strategy for high-spatiotemporal-resolution imaging of miRNA in living cells.

Near-infrared surface-enhanced Raman scattering (NIR-SERS) probes are promising for in vivo molecular imaging, but they face challenges in balancing plasmonic activity and signal reproducibility. We designed target-zippable anisotropic NIR gold nanorod (ani-NIR-AuNR) SERS probes, whose end and side regions are decorated with catalytic hairpin assembly (CHA) DNA hairpins and Raman reporters, respectively. These ani-NIR-AuNR monomers maintain a near-zero background until triggered by targets to form uniform side-by-side dimers with an average gap of 0.88 nm, synergistically amplifying electromagnetic enhancement and chemical enhancement. The CHA allows one target to zip numerous dimers, boosting hotspot density. These effects endow the SERS probes with good reproducibility (RSD = 8.56%), superior sensitivity (LOD = 0.15 fM), and a broad linear range (1 fM to 1 nM) for let-7d detection. Compared to fluorescence probes, they offer higher brightness, better spatial resolution, and longer signal persistence in in vivo miRNA imaging, demonstrating substantial potential in bioapplications.

Genotype imputation is essential for medical genomics studies. Herein, we present the STROMICS imputation reference panel, constructed from high-depth whole-genome sequencing (WGS) data of 10,241 Chinese individuals. It includes 53,061,655 single-nucleotide variants and insertion-deletions, spanning 22 autosomes and the X chromosome. Imputation performance of the STROMICS and seven other reference panels was compared using WGS data from 159 individuals. STROMICS panel outperformed others in imputation quality, and in genome-wide population- and individual-level accuracy. Validation using 301 Chinese individuals from the 1000 Genomes Project demonstrated STROMICS achieving high imputation accuracy. Among the three Chinese subgroups, the STROMICS reference panel yielded the highest accuracy in Han Chinese in Beijing samples. Notably, STROMICS outperformed all the other panels for the insertion-deletion imputation. When imputing stroke-risk variants and their closely linked variants with STROMICS, only a small statistically significant difference in sensitivity was observed between diseased and healthy individuals for variants closely linked to stroke-risk variants. Furthermore, calculated using pruned variants, the genetic distances between diseased and healthy groups remained largely unchanged before and after imputation. Collectively, these findings indicate that the source material used to construct the STROMICS reference panel has minimal impact on its imputation performance. Finally, we demonstrated high accuracy of STROMICS for genotype imputation in the X-unique region. In conclusion, the STROMICS panel provides a high-quality reference for imputing genotypes across autosomes and the X chromosome in the Chinese population.

Background:Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective:This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods:We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results:This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2-4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03-2.12], CCP3: 3.88 [3.78-3.97], and CCP4: 4.47 [4.33-4.63]) and CIN3+ (CCP2: 2.10 [2.05-2.14], CCP3: 3.92 [3.82-4.02], and CCP4: 4.45 [4.31-4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions:This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.

Background: An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for targeted interventions. However, such a model is currently lacking. This study leverages artificial intelligence to develop and validate a predictive model for post-stroke outcomes at three months and over five years, leveraging comprehensive data from the third China National Stroke Registry (CNSR-III), one of China largest nationwide multi-center ischemic stroke registries with five-year follow-up and the CHANCE-2 trial, a genotype-guided dual anti-platelet therapy trial in China. Methods: We evaluated 309 hospitalization variables, including baseline characteristics, medical history, hospitalization data, biomarkers, geographical factors, NIHSS/mRS scores, and stroke polygenic risk scores (PRS), using an extreme gradient boosting tree model. Feature importance was assessed via Shapley values. Primary outcomes were three-month stroke recurrence (5.6%), disability (mRS > 2, 13.75%), and mortality (1.18%). Secondary outcomes were assessed at six additional time points over five years. A nested cross-validation scheme was employed for feature selection and internal validation in 80% of patients (n=11,313) from CNSR-III cohort. External validation of the model was performed in the remaining 20% patients (n=2,627) from CNSR-III cohort and in CHANCE-2 trail (n=5,158). Results: Global and domain-specific delta-NIHSS(admission-discharge) emerged as the strongest predictor of stroke recurrence, disability and mortality. The Delta-NIHSS-Based Predictor for Post-Stroke Composite Outcomes (DISCO) model, integrating 16 delta-NIHSS(admission-to-discharge) and 8 clinically accessible variables, achieved AUCs > 0.8 for recurrence and disability and > 0.9 for mortality at three months. The highest-risk 1% patients exhibited a >10-fold relative risk (RR) for recurrence, >30-fold RR for disability, and >100-fold RR for mortality at three months. The DISCO model is clinically accessible at http://www.discosysu.cn. Conclusion: The DISCO model, incorporating 24 readily obtainable clinical variables, demonstrates high accuracy, robustness, clinical accessibility, and explainability in predicting post-stroke outcomes. The predictive strength of delta-NIHSS(admission-discharge) provides mechanistic insights into stroke outcomes and informs future acute stroke treatment and rehabilitation strategies. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement The Study was funded by Grants from National Key Research and Development Program of China (2022YFE0209600, 2022YFC2502400, 2022YFC2502402, 2022YFC2502404), the National Natural Science Foundation of China (82471304), the Young Elite Scientists Sponsorship Program by CAST (2023QNRC001) and Young Talents Supporting Program of Capital Medical University (B2417) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Ethics Committee of Beijing Tiantan Hospital (Institutional Review Board approval number: KY2015-001-01), as well as the ethics committees of all participating centers. Detailed information regarding ethical approvals can be found in the publications with PMID: 31709123 and 34708996. Based on the existing cohort, we are able to conduct this study involving prognostic model development, validation and explanation. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

ABSTRACT The assessment of human papillomavirus (HPV) genotype distribution could inform targeted cervical cancer prevention strategies. The epidemiology of HPV genotypes in terms of age and cervical lesions in Fujian Province, China has not been well described. This 9‐year retrospective study aimed to delineate the prevalence pattern and trend of HPV genotypes among a large‐scale community‐based population. Deidentified data were retrieved from the national cervical cancer screening program in China. We included eligible women aged 35–65 years who underwent cervical cancer screening between 2014 and 2022 in Fujian Province. The HPV prevalence within distinct subpopulations was calculated, and trends in HPV prevalence over the years and across age groups were examined using the Cochran‐Armitage trend test. A total of 551 604 women (median age 49 years [42, 54]; 0.10% with cervical cancer) were included in this study. The overall HPV prevalence was 11.72% (95% CI: 11.63%–11.80%), with HR‐HPV (high‐risk HPV) and HPV 16/18 prevalence at 10.02% (9.94%–10.10%) and 1.74% (1.71%–1.78%), respectively. HPV‐52, 58, 16, 39, 51, and 68 were the most predominant genotypes in the general population. Nearly all genotypes, except for HPV‐39 and 66, showed a decreasing trend in prevalence over the years, while a relatively high prevalence of HR‐HPV was observed across all age groups. As lesion severity increased, HR‐HPV and 9v‐HPV prevalence also increased. Our study underscores the importance of ongoing surveillance of HPV prevalence in China. While the overall decline in HPV infections over the years is encouraging, the relatively high prevalence of HR‐HPV warrants continued attention. Strengthening public health strategies—including prioritizing and promoting the current 9‐valent vaccination, extending HPV testing and cervical cancer screening to older women where feasible, and developing future vaccines targeting more HR‐HPV genotypes—will be crucial in eliminating cervical cancer and HPV‐related disease in China and beyond.