National Science Foundation Research Articles

Relational Resonance and Content Creation

This study investigates how tie formation shapes the production of user-generated content on social media, offering insights into enhancing user engagement and alleviating social media fatigue. We conceptualize tie formation as a relationship codevelopment process involving a tie initiator and a respondent. Drawing on social psychology literature, we propose a framework that traces how initiators’ social cognition evolves throughout this process—from proactively extending an inviting social tie to receiving a responsive one in return. Central to our framework is a perceptual mechanism we term relational resonance, which fosters intrinsic motivation and prompts initiators to adjust their content creation. Using data from YouTube, we develop a Bayesian hierarchical model to identify individual creators’ content adjustments, measured by changes in video volume and viewer engagement. Our results reveal that while initiators tend to reduce content production after receiving a responsive tie, the videos they create generate higher engagement, with “like” and “subscribe” rates increasing by 1.1 and 2.9 percentage points, respectively. Remarkably, the receipt of a responsive tie leads to average weekly increases of 66 likes and 183 subscriptions even after accounting for the decrease in video volume, reinforcing the “quality over quantity” principle in content creation strategies. An online experiment further elucidates the mechanism underlying the observed adjustment behavior, providing robust validation for our proposed framework. Implications for research and practice are discussed. This paper was accepted by D.J. Wu, information systems. Funding: T. Song was supported by National Natural Science Foundation of China [Grants 71902114 and 72372101]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00830 .

Variational quantum algorithm for designing quantum information maskers* *Supported by the National Natural Science Foundation of China (under Grant Nos. 12105090 and 12074107), the Program of Outstanding Young and Middle-aged Scientific and Technological Innovation Team of Colleges and Universities in Hubei Province of China (under Grant No. T2020001), and the Innovation Group Project of the Natural Science Foundation of Hubei

Since the concept of quantum information masking was proposed by Modi et al (2018 Phys. Rev. Lett. 120, 230 501), many interesting and significant results have been reported, both theoretically and experimentally. However, designing a quantum information masker is not an easy task, especially for larger systems. In this paper, we propose a variational quantum algorithm to resolve this problem. Specifically, our algorithm is a hybrid quantum–classical model, where the quantum device with adjustable parameters tries to mask quantum information and the classical device evaluates the performance of the quantum device and optimizes its parameters. After optimization, the quantum device behaves as an optimal masker. The loss value during optimization can be used to characterize the performance of the masker. In particular, if the loss value converges to zero, we obtain a perfect masker that completely masks the quantum information generated by the quantum information source, otherwise, the perfect masker does not exist and the subsystems always contain the original information. Nevertheless, these resulting maskers are still optimal. Quantum parallelism is utilized to reduce quantum state preparations and measurements. Our study paves the way for wide application of quantum information masking, and some of the techniques used in this study may have potential applications in quantum information processing.

Stochastic First-Order Methods for Average-Reward Markov Decision Processes

We study average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy optimization and policy evaluation. Compared with intensive research efforts in finite sample analysis of policy gradient methods for discounted MDPs, existing studies on policy gradient methods for AMDPs mostly focus on regret bounds under restrictive assumptions, and they often lack guarantees on the overall sample complexities. Toward this end, we develop an average-reward stochastic policy mirror descent method for solving AMDPs with and without regularizers and provide convergence guarantees in terms of the long-term average reward. For policy evaluation, existing on-policy methods suffer from suboptimal convergence rates as well as failure in handling insufficiently random policies due to the lack of exploration in the action space. To remedy these issues, we develop a variance-reduced temporal difference (VRTD) method with linear function approximation for randomized policies along with optimal convergence guarantees, and design an exploratory VRTD method that resolves the exploration issue and provides comparable convergence guarantees. By combining the policy evaluation and policy optimization parts, we establish sample complexity results for solving AMDPs under both generative and Markovian noise models. It is worth noting that when linear function approximation is utilized, our algorithm only needs to update in the low-dimensional parameter space, and thus can handle MDPs with large state and action spaces. Funding: T. Li and G. Lan were supported in part by the National Science Foundation [Grant CCF-1909298] and the Office of Navel Research [Grant N00014-20-1-2089].

ℓp-Sphere Covering and Approximating Nuclear p-Norm

The spectral p-norm and nuclear p-norm of matrices and tensors appear in various applications, albeit both are NP-hard to compute. The former sets a foundation of [Formula: see text]-sphere-constrained polynomial optimization problems, and the latter has been found in many rank minimization problems in machine learning. We study approximation algorithms of the tensor nuclear p-norm with an aim to establish the approximation bound matching the best one of its dual norm, the tensor spectral p-norm. Driven by the application of sphere covering to approximate both tensor spectral and nuclear norms ([Formula: see text]), we propose several types of hitting sets that approximately represent the [Formula: see text]-sphere with adjustable parameters for different levels of approximations and cardinalities, providing an independent toolbox for decision making on [Formula: see text]-spheres. Using the idea in robust optimization and second-order cone programming, we obtain the first polynomial-time algorithm with an [Formula: see text]-approximation bound for the computation of the matrix nuclear p-norm when [Formula: see text] is a rational, paving a way for applications in modeling with the matrix nuclear p-norm. These two new results enable us to propose various polynomial-time approximation algorithms for the computation of the tensor nuclear p-norm using tensor partitions, convex optimization, and duality theory, attaining the same approximation bound to the best one of the tensor spectral p-norms. Effective performance of the proposed algorithms for the tensor nuclear p-norm is shown by numerical implementations. We believe the ideas of [Formula: see text]-sphere covering with its applications in approximating nuclear p-norm would be useful for tackling optimization problems on other sets, such as the binary hypercube with its applications in graph theory and neural networks and the nonnegative sphere with its applications in copositive programming and nonnegative matrix factorization. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71825003, 72394360, 72394364, 72171141, 72192830, and 72192832], and the program for Innovative Research Team of Shanghai University of Finance and Economics.

Arriba y Adelante: Looking Outward and Moving Ahead.

Judith E. Deutsch, PT, PhD, FAPTA, the 55th McMillan Lecturer, is professor and director of the Research in Virtual Environments and Rehabilitation Sciences (Rivers) Lab in the Doctoral Programs in Physical Therapy in the School of Health Professions at Rutgers University. Her current research includes the development and testing of virtual reality, serious games to improve mobility and fitness of individuals with neurologic health conditions, and knowledge translation to strengthen evidence-based practice. Her research has been funded by the National Institutes of Health, the National Science Foundation, and the American Hospital Association. She also has longstanding interest and scholarship in integrating complementary therapies into physical therapy. Dr Deutsch has been recognized with awards for teaching, research, mentoring, service and publications from the American Physical Therapy Association (APTA), Academy of Neurologic Physical Therapy, the New Jersey chapter of APTA, the International Society for Virtual Rehabilitation, the Kessler Institute for Rehabilitation, the University of Southern California, and Rutgers University.

Microbiome and Metabolomics Reveal the Effect of Gut Microbiota on Liver Regeneration of Fatty Liver Disease

Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with impaired regenerative capacity and poor postoperative prognosis following hepatectomy. Previous research has highlighted the importance of the gut-liver axis in the physiological and pathological processes of the liver. However, the contribution of gut bacteria to the regeneration of livers with MAFLD and its metabolic regulatory mechanisms remain elusive. Partial hepatectomy (PHx) was performed on C57Bl/6J mice fed with high-fat diet (HFD) for 12 weeks. Pathological examination, immunohistochemistry, and qRT-PCR analysis were performed to assess the severity of steatosis and proliferative potential. The gut microbiome was examined by 16S rRNA gene sequencing and shotgun metagenomics, whereas liver metabolomics was analysed via untargeted and targeted metabolomics using liquid chromatography-tandem mass spectrometry (LC-MS). HFD-induced hepatic steatosis in mice led to impaired liver regeneration following PHx. The gut microbiota and liver metabolites were altered along with the liver regeneration process. Longitudinal time-series analysis revealed dynamic alterations in these data, whereas correlation analysis screened out bacterial candidates that potentially influence liver regeneration in MAFLD by modulating metabolic pathways. Among these bacteria, the dominant bacterium Akkermansia was selected for subsequent investigation. MAFLD mice gavaged with Akkermansia muciniphila (A.muciniphila) exhibited reduced liver lipid accumulation and accelerated liver regeneration, possibly through the regulation of the tricarboxylic acid (TCA) cycle. These data demonstrated the interplay between the gut microbiome, liver metabolomics, and liver regeneration in mice with MAFLD. A.muciniphila has the potential to serve as a clinical intervention agent to accelerate postoperative recovery in MAFLD. This work was supported by the Research Project of Jinan Microecological Biomedicine Shandong Laboratory [JNL-2022008B]; the Zhejiang Provincial Natural Science Foundation of China [LZ21H180001]; the Fundamental Research Funds for the Central Universities [No. 2022ZFJH003].

Advances in MRI Research for First-Episode Schizophrenia: A Selective Review and NSFC-Funded Analysis.

The causes of schizophrenia remain unclear, and research has been hindered by the lack of quantifiable standards. However, magnetic resonance imaging (MRI) is addressing these challenges, revealing critical neurobiological details and emphasizing its importance in both evaluation and treatment. First, we reviewed the progress of research on structural MRI (sMRI), functional MRI (fMRI), multimodal/multiomics analysis, artificial intelligence, and neuromodulation in first-episode schizophrenia (FES) over the past 5 years. Second, we summarize the current state of schizophrenia research funded by the National Natural Science Foundation of China (NSFC) to facilitate academic exchange and cooperation both domestically and internationally. sMRI has identified early neurodevelopmental biomarkers in FES patients, and fMRI has highlighted functional abnormalities across disease stages. Multimodal/multiomics analysis has revealed complex brain-neurobiology interactions. Neuromodulation techniques, which directly modulate neural activity in specific brain regions, offer promising long-term benefits for stabilizing conditions and enhancing patients' quality of life. NSFC-funded analysis shows China is increasing its funding for schizophrenia research, though funding distribution remains uneven. The research focus has shifted from a single perspective on brain structure and function to multichannel, multimodal comprehensive analysis methods. This progress has driven the integration of machine learning-driven multiomics research, aiming to construct disease classification models, explore disease mechanisms, and guide treatment from multidimensional and interdisciplinary perspectives. MRI technology has provided new perspectives for the diagnosis and treatment of schizophrenia, especially the neurobiological foundations of the disease. Support from the NSFC provides a scientific and financial basis for future research and treatment, heralding scientific discoveries and technological innovations in this field and bringing hope to schizophrenia patients.

Provocations from the ‘STS as a Critical Pedagogy’ Workshop

This research article is a collaborative set of reflections and provocations stemming from the National Science Foundation (NSF) funded workshop on STS as a Critical Pedagogy, hosted online during the summer of 2021 by Shannon N. Conley and Emily York at James Madison University. The workshop occurred over four separate sessions, bringing together forty participants (including six undergraduate students who contributed as both facilitators and research assistants). Participants self-organized into panels, leading the workshop collective to engage a host of questions, challenges, methods, and practices related to STS and critical pedagogy. Questions included the following. What characterizes critical STS pedagogies? How are critical STS pedagogies enabled and constrained by our institutional and disciplinary locations? What makes STS pedagogies travel? How might we imagine STS pedagogies otherwise? How do our pedagogies shape our research and engagement in the world? How might we critically interrogate the boundaries between research, teaching, service, and engagement, and what becomes visible when we do so?

Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies such as cancer screening and tobacco treatment, which are currently under-utilised. Polygenic risk scores (PRSs) may further motivate health behaviour change in primary care for lung cancer in diverse populations. In this work, we introduce the GREAT care paradigm, which integrates PRSs within comprehensive patient risk profiles to motivate positive health behaviour changes. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardised PRS distributions across all ancestries. We validated our PRSs in 561,776 individuals of diverse ancestry from the GISC Trial, UK Biobank (UKBB), and All of Us Research Program (AoU). Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58-2.18) in UKBB and 2.39 (95% CI: 1.93-2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32-1.41) in UKBB and 1.32 (95% CI: 1.28-1.36) in AoU. Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations, which will be evaluated in two cluster-randomised clinical trials. This approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment. National Institutes of Health, NIH Intramural Research Program, National Science Foundation.

Which experimental factors govern successful animal-to-human translation in multiple sclerosis drug development? A systematic review and meta-analysis

Despite successes in multiple sclerosis (MS) drug development, the effectiveness of animal studies in predicting successful bench-to-bedside translation is uncertain. Our goal was to identify predictors of successful animal-to-human translation for MS by systematically comparing animal studies of approved disease-modifying therapies (DMTs) with those that failed in clinical trials due to efficacy or safety concerns. Systematic review of animal studies testing MS DMTs, identified from searches in PubMed and EMBASE. A random effect meta-analysis was fitted to the data to compare outcome effect sizes for approved versus failed DMTs. Effect sizes and testing under diverse experimental conditions were assessed as potential predictors for successful translation. We included 497 animal studies, covering 15 approved and 11 failed DMTs, tested in approximately 30'000 animals. DMTs were tested in a small repertoire of experimental parameters: about 86% of studies used experimental autoimmune encephalomyelitis (EAE), 80% used mice, and 76% used female animals. There was no association between animal study outcomes or testing DMTs under varied conditions (e.g., different laboratories or models) and successful approval. Surprisingly, 91% of animal studies were published after first-in-MS trial and 91% after official regulatory approval. Our findings emphasize the complexity in carrying drugs from animals to clinical practice. Specific challenges include limited experimental methods in animal research and a disconnect between preclinical and clinical research. We advocate for efforts to streamline drug development for MS to improve animal research's relevance for patients. NIH, Swiss National Science Foundation, Universities Federation for Animal Welfare.

The Story Behind the Biology

Students possess many misconceptions about the nature of science and science practices that hinder robust understanding of science content, diminish interest in science learning, make science appear as an uninteresting career option, and exacerbate distrust of science. With National Science Foundation funding, freely and easily accessible historically accurate short stories have been created that bring science and scientists to life; accurately illustrating the work of scientists and how scientific knowledge is developed and becomes well established. This article presents the project’s biology stories, and focuses on the classroom implementation of one story to illustrate mediation strategies that draw students’ attention to targeted nature of science and science practices. Research supporting the efficacy of this approach is summarized.

Associations of long-term nitrogen dioxide exposure with a wide spectrum of diseases: a prospective cohort study of 0·5 million Chinese adults

Little evidence is available on the long-term health effects of nitrogen dioxide (NO2) in low-income and middle-income populations. We investigated the associations of long-term NO2 exposure with the incidence of a wide spectrum of disease outcomes, based on data from the China Kadoorie Biobank. This prospective cohort study involved 512 724 Chinese adults aged 30-79 years recruited from ten areas of China during 2004-08. Time-varying Cox regression models yielded adjusted hazard ratios (HRs) for the associations of long-term NO2 exposure with aggregated disease incidence endpoints classified by 14 ICD-10 chapters, and incidences of 12 specific diseases selected from three key ICD-10 chapters (cardiovascular, respiratory, and musculoskeletal diseases) found to be robustly associated with NO2 in the analyses of aggregated endpoints. All models were stratified by age-at-risk (in 1-year scale), study area, and sex, and were adjusted for education, household income, smoking status, alcohol intake, cooking fuel type, heating fuel type, self-reported health status, BMI, physical activity level, temperature, and relative humidity. The analysis of 512 709 participants (mean baseline age 52·0 years [SD 10·7]; 59·0% female and 41·0% male) included approximately 6·5 million person-years of follow-up. Between 5285 and 144 852 incident events were recorded for each of the 14 aggregated endpoints. Each 10 μg/m3 higher annual average NO2 exposure was associated with higher risks of chapter-specific endpoints, especially cardiovascular (n=144 852; HR 1·04 [95% CI 1·02-1·05]), respiratory (n=73 232; 1·03 [1·01-1·05]), musculoskeletal (n=54 409; 1·11 [1·09-1·14]), and mental and behavioural (n=5361; 1·12 [1·05-1·21]) disorders. Further in-depth analyses on specific diseases found significant positive supra-linear associations with hypertensive disease (1·08 [1·05-1·11]), lower respiratory tract infection (1·03 [1·01-1·06]), arthrosis (1·15 [1·09-1·21]), intervertebral disc disorders (1·13 [1·09-1·17]), and spondylopathies (1·05 [1·01-1·10]), and linear associations with ischaemic heart disease (1·03 [1·00-1·05]), ischaemic stroke (1·08 [1·06-1·11]), and asthma (1·15 [1·04-1·27]), whereas intracerebral haemorrhage (1·00 [0·95-1·06]), other cerebrovascular disease (0·98 [0·96-1·01]), acute upper respiratory infection (1·03 [0·96-1·09]), and chronic lower respiratory disease (0·98 [0·95-1·02]) showed no significant association. NO2 exposure showed robust null association with external causes (n=32 907; 0·98 [0·95-1·02]) as a negative control. In China, long-term NO2 exposure was associated with a range of diseases, particularly cardiovascular, respiratory, and musculoskeletal diseases. These associations underscore the pressing need to implement the recently tightened WHO air quality guidelines. Wellcome Trust, UK Medical Research Council, Cancer Research UK, British Heart Foundation, National Natural Science Foundation of China, National Key Research and Development Program of China, Sino-British Fellowship Trust, and Kadoorie Charitable Foundation.

Development and validation of the MRI-based deep learning classifier for distinguishing perianal fistulizing Crohn’s disease from cryptoglandular fistula: a multicenter cohort study

A singular reliable modality for early distinguishing perianal fistulizing Crohn's disease (PFCD) from cryptoglandular fistula (CGF) is currently lacking. We aimed to develop and validate an MRI-based deep learning classifier to effectively discriminate between them. The present study retrospectively enrolled 1054 patients with PFCD or CGF from three Chinese tertiary referral hospitals between January 1, 2015, and December 31, 2021. The patients were divided into four cohorts: training cohort (n=800), validation cohort (n=100), internal test cohort (n=100) and external test cohort (n=54). Two deep convolutional neural networks (DCNN), namely MobileNetV2 and ResNet50, were respectively trained using the transfer learning strategy on a dataset consisting of 44871MR images. The performance of the DCNN models was compared to that of radiologists using various metrics, including receiver operating characteristic curve (ROC) analysis, accuracy, sensitivity, and specificity. Delong testing was employed for comparing the area under curves (AUCs). Univariate and multivariate analyses were conducted to explore potential factors associated with classifier performance. A total of 532 PFCD and 522 CGF patients were included. Both pre-trained DCNN classifiers achieved encouraging performances in the internal test cohort (MobileNetV2 AUC: 0.962, 95% CI 0.903-0.990; ResNet50 AUC: 0.963, 95% CI 0.905-0.990), as well as external test cohort (MobileNetV2 AUC: 0.885, 95% CI 0.769-0.956; ResNet50 AUC: 0.874, 95% CI 0.756-0.949). They had greater AUCs than the radiologists (all p≤0.001), while had comparable AUCs to each other (p=0.83 and p=0.60 in the two test cohorts). None of the potential characteristics had a significant impact on the performance of pre-trained MobileNetV2 classifier in etiologic diagnosis. Previous fistula surgery influenced the performance of the pre-trained ResNet50 classifier in the internal test cohort (OR 0.157, 95% CI 0.025-0.997, p=0.05). The developed DCNN classifiers exhibited superior robustness in distinguishing PFCD from CGF compared to artificial visual assessment, showing their potential for assisting in early detection of PFCD. Our findings highlight the promising generalized performance of MobileNetV2 over ResNet50, rendering it suitable for deployment on mobile terminals. National Natural Science Foundation of China.

A complex intervention to reduce antibiotic prescribing in rural China: a cluster randomised controlled trial

Excessive use of antibiotics is a widespread problem. We aim to evaluate the efficacy of a multifaceted intervention for reducing antibiotic use in patients with respiratory tract infections (RTIs). In this two-arm cluster randomized controlled trial, we enrolled patients aged 18+ with symptomatic RTIs at 40 township health centers (THCs) selected from 10 counties in Anhui, China. The THCs were randomized using an online tool ('Sealed Envelope') to intervention or usual care (1:1 ratio), stratified by baseline antibiotic prescribing and with random block sizes (4 or 6). The intervention had five components: a half-day clinician training, a WeChat-based peer support group, a decision aid, a poster commitment letter and a patient leaflet. The primary outcome was whether antibiotics were prescribed at the index consultation. Secondary measures included defined daily dose (DDD), illness recovery rate, re-visits to other care-givers or retail pharmacies and incremental cost-effectiveness ratio (ICER). These measures were analyzed using generalized linear mixed modeling controlling for clustering. The study was registered as ISRCTN30652037. Between December 2021 and September 2022, 1053 patients were recruited (intervention, 21THCs, n=552; control, 19THCs, n=501), using consecutive sampling. Antibiotic prescribing rate was 55.25% and 66.67% in the intervention and control arms (Odds ratio 0.52, 95% confidence interval [CI]: 0.27, 0.98; p=0.044). The intervention group also had lower, significant or non-significant, differences for other markers of antibiotic use: DDD (1.57 vs 2.75); prescriptions of two or more types of antibiotics (9.78% vs 11.58%); obtaining antibiotics from retail pharmacies (3.68% vs 5.78) or from other clinics (2.70% vs 4.05%). The intervention resulted in a cost reduction of 9.265 RMB (1.471 USD) per consultation episode and an ICER of-7769.98 RMB or-1233.33 USD/QALYs. The intervention did not encounter any major adverse event. The intervention package was effective and cost-effective in reducing antibiotics prescribing without adverse effects. The trial was supported by National Natural Science Foundation of China (No. 81861138049) and United Kingdom Research Innovation (No. MR/S013717/1).

Systematic identification of pathogenic variants of non-small cell lung cancer in the promoters of DNA-damage repair genes

Deficiency in DNA-damage repair (DDR) genes, often due to disruptive coding variants, is linked to higher cancer risk. Our previous study has revealed the association between rare loss-of-function variants in DDR genes and the risk of lung cancer. However, it is still challenging to study the predisposing role of rare regulatory variants of these genes. Based on whole-genome sequencing data from 2984 patients with non-small cell lung cancer (NSCLC) and 3020 controls, we performed massively parallel reporter assays on 1818 rare variants located in the promoters of DDR genes. Pathway- or gene-level burden analyses were performed using Firth's logistic regression or generalized linear model. We identified 750 rare functional regulatory variants (frVars) that showed allelic differences in transcriptional activity within the promoter regions of DDR genes. Interestingly, the burden of frVars was significantly elevated in cases (odds ratio [OR]=1.17, p=0.026), whereas the burden of variants prioritized solely based on bioinformatics annotation was comparable between cases and controls (OR=1.04, p=0.549). Among the frVars, 297 were down-regulated transcriptional activity (dr-frVars) and 453 were up-regulated transcriptional activity (ur-frVars); especially, dr-frVars (OR=1.30, p=0.008) rather than ur-frVars (OR=1.06, p=0.495) were significantly associated with risk of NSCLC. Individuals with NSCLC carried more dr-frVars from Fanconi anemia, homologous recombination, and nucleotide excision repair pathways. In addition, we identified seven genes (i.e., BRCA2, GTF2H1, DDB2, BLM, ALKBH2, APEX1, and RAD51B) with promoter dr-frVars that were associated with lung cancer susceptibility. Our findings indicate that functional promoter variants in DDR genes, in addition to protein-truncating variants, can be pathogenic and contribute to lung cancer susceptibility. National Natural Science Foundation of China, Youth Foundation of Jiangsu Province, Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer of Chinese Academy of Medical Sciences, and Natural Science Foundation of Jiangsu Province.

Contraception with levonorgestrel-releasing intrauterine system versus copper intrauterine device: a meta-analysis of randomized controlled trials

Globally, approximately 19.4% of women of reproductive age use intrauterine contraception, encompassing both copper intrauterine devices (Cu-IUDs) and levonorgestrel intrauterine devices (LNG-IUDs). Despite current guidelines endorsing intrauterine contraception as a primary method, there remains debate regarding device selection. Notably, the lack of data regarding reasons for discontinuation has limited previous meta-analyses. This study aims to comprehensively evaluate the potential differences between intrauterine devices using available multinational data, thereby providing a basis for global policy and healthcare services. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane Library for primary studies published from inception to January 13, 2024, with no language or geographic restrictions. The study was registered on PROSPERO (CRD42024496400). We included only randomized controlled trials comparing Cu-IUDs and LNG-IUDs. Data extraction was independently conducted by two reviewers, with unresolved discrepancies referred to a third senior reviewer for consultation. The primary outcome was pregnancy, with secondary outcomes encompassing continuation, reasons for discontinuation, expulsion, satisfaction, and other adverse events. Data were synthesized using a random-effects model. Risk of bias was evaluated with the Cochrane Collaboration's tool, and evidence quality was assessed using the GRADE framework. An analysis of 20 trials showed that compared to Cu-IUDs, LNG-IUDs were associated with lower risks of pregnancy (Risk Ratio 0.22, 95% confidence interval 0.12-0.39), ectopic pregnancy (RR 0.12, 95% CI 0.03-0.47), discontinuation due to increased bleeding (RR 0.49, 95% CI 0.28-0.85), increased bleeding (RR 0.42, 95% CI 0.25-0.7), heavy bleeding (RR 0.41, 95% CI 0.22-0.75), and dysmenorrhea (RR 0.41, 95% CI 0.34-0.48), but they carried a higher risk of discontinuation due to amenorrhea (RR 21.05, 95% CI 8.83-50.00). When comparing LNG (52mg) IUD with copper (380mm2) IUD, The LNG-IUD showed a lower risk of discontinuation due to increased bleeding (RR 0.68, 95% CI 0.55-0.58) and dysmenorrhea (RR 0.42, 95% CI 0.34-0.53), but a higher risk of discontinuation due to bleeding issues (RR 2.83, 95% CI 2.47-3.25) and amenorrhea (RR 5.92, 95% CI 2.81-12.49). There were no significant differences between the two terms of continuation, expulsion, non-medical reasons for discontinuation, satisfaction, and other adverse outcomes. LNG-IUDs and Cu-IUDs are both highly effective contraceptive methods. Compared to Cu-IUDs, LNG-IUDs were associated with a lower risk of pregnancy and adverse reactions. However, LNG-IUDs carry a higher risk of amenorrhea. When recommending contraceptive methods, healthcare providers should fully inform patients of these potential risks and consider patient preferences. The research was funded by Hunan Provincial Natural Foundation of China (2021JJ30040), the National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University (2021KFJJ06), and the National Natural Science Foundation of China (No. 81672225).

Global, regional, and national burden of dengue infection in children and adolescents: an analysis of the Global Burden of Disease Study 2021

Dengue remains a global threat to public health, however, its disease burden among children and adolescents has always been poorly quantified. Therefore, we aimed to systematically estimate the global, regional, and national burden and temporal trends of dengue infection in children and adolescents from 1990 to2021. Data on the number and rate of incidence and disability-adjusted life-years (DALYs) of dengue infection among children and adolescents under 20 years were obtained from the Global Burden of Disease Study (GBD) 2021. Estimated annual percentage changes (EAPCs) of the age-standardised incidence rate (ASIR) and age-standardised DALYs rate (ASDR), by age, sex, and different countries and regions, were calculated to quantify the temporal trends of dengue burden. The association between development levels (measured using the socio-demographic index [SDI]) and dengue burden was also determined. From 1990 to 2021, the global burden of dengue incidence and its associated DALYs was consistently higher in children and adolescents than in the entire population. Globally, among children and adolescents, there were 21,641,016 incident cases in 2021, an increase of 64.43% compared to 13,241,719 in 1990. The ASIR per 100,000 population increased from 587.81 in 1990 to 812.16 in 2021, with an EAPC of 1.39% (95% confidence interval [CI], 1.13%-1.65%). Besides, the number of dengue-associated DALYs among children and adolescents increased by 16.36%, from 910,458.60 in 1990 to 1,059,428.31 in 2021. The increase in DALYs was less pronounced than incidence, with the ASDR per 100,000 population increasing from 40.17 in 1990 to 41.50 in 2021, and the EAPC was only 0.67% (95% CI, 0.40%-0.95%). The incidence and DALYs burden of dengue in children and adolescents was highest in middle SDI regions, followed by low-middle SDI regions, with the lowest burden in high SDI regions. Furthermore, Tropical Latin America had an extremely high ASIR (6040.29 per 100,000 population in 2021), and Southeast Asia had an extremely high ASDR (298.20 per 100,000 population in 2021), much higher than other regions around the world. The global dengue burden in children and adolescents is high and has been increasing from 1990 to 2021, even though the distribution patterns vary across different countries and territories. This study first reported the global disease burden and temporal trends of children and adolescents, which has significant implications for policymakers and public health officials, as it underscores the need for age- and region-specific strategies to mitigate the growing global burden of dengue. National Natural Science Foundation of China.

Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening. Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors' assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated. In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p=0.50; SEN: 81.8 vs. 88.6, p=0.16; SPE: 89.2 vs. 87.6, p=0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p=0.00015; SEN: 79.5 vs. 74.6, p=0.029; SPE: 81.7 vs. 75.8, p=0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p=0.80; SEN: 84.8 vs. 82.6, p=0.72; SPE: 81.2 vs. 81.2, p>0.99). The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer. This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

Multi-omic characteristics of longitudinal immune profiling after breakthrough infections caused by Omicron BA.5 sublineages

Omicron sub-variants breakthrough infections (BTIs) have led to millions of coronavirus disease 2019 (COVID-19) cases worldwide. The acute-phase immune status is critical for prognosis, however, the dynamic immune profiling of COVID-19 during the first month after BTIs remains unclear. In this study, we monitored the immune dynamics at various timepoints in a longitudinal cohort during the first month post-BTIs through clinical evaluation, single-cell RNA sequencing (scRNA-seq), T cell receptor (TCR)/B cell receptor (BCR) sequencing, and antibody mass spectrometry. Serological analysis revealed limited impairment to functions of major organs, active cellular and humoral immunity at 2 weeks post-BTI, with significant increases in cytokines (CKs) and neutralizing antibody levels. However, 1 month post-BTI, organ function parameters and CK levels reverted to pre-infection levels, whereas neutralizing antibody levels remained high. Notably, scRNA-seq showed that lymphocytes maintained strong antiviral activity and cell depletion at 2 weeks and 1 month post-BTI, with genes CD81, ABHD17A, CXCR4, DUSP1, etc. upregulated, and genes PFDN5, DYNLRB1, CD52, etc. downregulated, indicating that lymphocytes status take longer to recover to normal levels than that routine blood tests revealed. Additionally, T cell-exhaustion associated genes, including LAG3, TIGIT, PDCD1, CTLA4, HAVCR2, and TOX, were upregulated after BTI. TCRs and BCRs exhibited higher clonotypes, mainly in CD8Tem or plasmablast cells, at 2 weeks post-BTI comparing 1 month. More IgG and IgA-type BCRs were found in the groups of 1 month post-BTI, with higher somatic hypermutation, indicating greater maturity. Verification of monoclonal antibodies corresponding to amplified BCRs highlighted the antigen-specific and broad-spectrum characteristics. Our study elucidated the dynamic immune profiling of individuals after Omicron BA.5 sublineages BTI. Strong immune activation, antiviral response, antibody maturation and class transition at 2 weeks and 1 month after BTI may provide essential insights into pathogenicity, sequential immune status, recovery mechanisms of Omicron sublineage BTI. This study was supported by the National Key R&D Program of China, the China Postdoctoral Science Foundation, Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, CAS Project for Young Scientists in Basic Research, and the Air Force Special Medical Center Science and Technology Booster Program.

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides. In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set). Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52-67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77-1·00) to 0·97 (0·93-1·00) and accuracy values ranged from 0·91 (0·85-0·98) to 0·97 (0·93-1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80-0·91) to 0·95 (0·86-1·00) and accuracy values ranged from 0·79 (0·74-0·85) to 0·99 (0·98-1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI 0·75-0·85) to 0·91 (0·88-1·00) and accuracy values ranged from 0·79 (0·76-0·82) to 0·95 (0·93-0·96); for aspiration biopsy samples, AUC values ranged from 0·76 (0·70-0·83) to 0·87 (0·80-0·94) and accuracy values ranged from 0·76 (0·74-0·79) to 0·97 (0·96-0·98). The model also showed strong performance on the TCGA dataset (473 patients; 535 slides; AUC values ranged from 0·82 [95% CI 0·71-0·93] to 0·96 [0·91-1·00], accuracy values ranged from 0·79 [0·70-0·88] to 0·95 [0·90-1·00]). The DeepGEM model, trained on primary region biopsy samples, could be generalised to biopsy samples from lymph node metastases, with AUC values of 0·91 (95% CI 0·88-0·94) for EGFR and 0·88 (0·82-0·93) for KRAS and accuracy values of 0·85 (0·80-0·88) for EGFR and 0·95 (0·92-0·96) for KRAS and showed potential for prognostic prediction of targeted therapy. The model generated spatial gene mutation maps, indicating gene mutation spatial distribution. We developed an AI-based method that can provide an accurate, timely, and economical prediction of gene mutation and mutation spatial distribution. The method showed substantial potential as an assistive tool for guiding the clinical treatment of patients with lung cancer. National Natural Science Foundation of China, the Science and Technology Planning Project of Guangzhou, and the National Key Research and Development Program of China. For the Chinese translation of the abstract see Supplementary Materials section.