Military Medical Research最新文献

{"title":"Prioritizing human-AI collaboration in healthcare: the TRIAD framework for trustworthy governance, real-world, and integrated adaptive deployment.","authors":"Jia Li, Zi-Chun Zhou, Zhen-Chang Wang, Han Lv","doi":"10.1186/s40779-026-00684-w","DOIUrl":"https://doi.org/10.1186/s40779-026-00684-w","url":null,"abstract":"<p><p>Artificial intelligence (AI) and big data are reshaping the healthcare landscape. However, clinical value depends on how well systems augment clinicians and fit into routine workflows. To this end, we introduce the TRIAD framework: trustworthy governance, real-world clinical value, and integrated adaptive deployment, to guide the development, validation, and deployment of clinical AI. TRIAD requires explicit data provenance and intended use, fairness auditing, and calibrated uncertainty. This framework evaluates the human-AI team in real workflows using team-level metrics, including accuracy, safety, workload, and patterns of acceptance, editing, and overriding. Deployment proceeds via staged rollouts with preregistered guardrails and continuous monitoring of performance and subgroup impact. TRIAD views intelligence as a property of the human-AI team rather than the AI model alone. Aligning governance, evaluation, and deployment around clinicians and patients enables durable gains in safety, equity, efficiency, and experience, thereby elevating clinical value.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"12 ","pages":"97"},"PeriodicalIF":22.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147434192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RNA as a genome architect: G-loops in G-quadruplex regulation.","authors":"Jie Wang, Zhao-Jie Lyu, Qi Zhang, William C Cho, De-Chao Feng","doi":"10.1186/s40779-025-00683-3","DOIUrl":"10.1186/s40779-025-00683-3","url":null,"abstract":"","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"12 1","pages":"96"},"PeriodicalIF":22.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12801905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The \"cytokine storm\" in infection and sepsis: win the battle but lose the war.","authors":"Jiang-Bo Fan, Qin-Yuan Li, Xi-Feng Feng, Si-Yuan Huang, Rui Wang, Feng-Ying Liao, Di Liu, Wen-Yi Liu, Jian-Hui Sun, Hua-Cai Zhang, Hui-Ting Zhou, Jian-Xin Jiang, Zhen Wang, Ling Zeng","doi":"10.1186/s40779-025-00678-0","DOIUrl":"10.1186/s40779-025-00678-0","url":null,"abstract":"<p><p>The cytokine storm, a life-threatening systemic inflammatory syndrome, is the primary driver of multiorgan failure in different clinical situations, including severe infections, autoimmune diseases, chimeric antigen receptor (CAR) T cell immunotherapy for cancer, and genetic syndromes. This review focuses primarily on cytokine storms triggered by severe infections such as viral pneumonia and bacterial sepsis, and explores the underlying mechanisms of cytokine storms and potential therapeutic interventions. Cytokine storms are characterized primarily by the excessive release of proinflammatory cytokines, which are triggered by pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), and PANoptosis, all of which activate immune signaling cascades. Amplification mechanisms involve positive feedback loops and the failure of negative feedback mechanisms, leading to uncontrolled inflammation. Like a pyrrhic victory, the excessive activation of the immune system eliminated invading pathogens but caused catastrophic damage due to multiple organ dysfunction syndrome (MODS), turning the life-saving response into a life-threatening war. Therapeutic strategies, including cytokine antagonists, Janus kinase (JAK) inhibitors, caspase inhibitors, glucocorticoids, and blood purification therapies, aim to interrupt the self-amplifying cycle of inflammation that propagates organ injury, thereby reducing MODS and mortality. Challenges include optimizing the treatment timing and patient stratification. Future research should focus on combination therapies and personalized medicine based on the heterogeneity of infections and sepsis. Advances in multiomics and targeted therapies provide new hope for managing infections and sepsis.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"12 1","pages":"95"},"PeriodicalIF":22.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12794442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer.","authors":"Yu Wang, Yong-Bo Xiang, Xiao-Wei Chen, Tao Zhang, Jian-Yang Wang, Wen-Yang Liu, Lei Deng, Lu-Hua Wang, Shu-Geng Gao, Nan Bi","doi":"10.1186/s40779-025-00679-z","DOIUrl":"10.1186/s40779-025-00679-z","url":null,"abstract":"<p><strong>Background: </strong>Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.</p><p><strong>Methods: </strong>Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.</p><p><strong>Results: </strong>A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.</p><p><strong>Conclusions: </strong>As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"12 1","pages":"94"},"PeriodicalIF":22.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12771999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.","authors":"Xiu-Ming Zhang, Tian-Hong Gao, Qiu-Yu Cai, Jia-Bin Xia, Yu-Ning Sun, Jian Yang, Wei-Han Li, Sheng-Xu-Ming Zhang, Heng-Rui Lou, Xiao-Tian Yu, Kai-Wen Hu, Jing-Wen Ye, Jin-Xing Zhang, Jie Lei, Le-Chao Cheng, Lin-Jie Xu, Qing Chen, He-Xiang Wang, Mei-Fu Gan, Cheng Lu, Nan Pu, Ming-Li Song, Xin Chen, Wen-Jie Liang, Han Lv, Chao-Qing Xu, Zai-Yi Liu, Jing Zhang, Kai Yan, Zun-Lei Feng","doi":"10.1186/s40779-025-00680-6","DOIUrl":"10.1186/s40779-025-00680-6","url":null,"abstract":"<p><p>Artificial intelligence (AI) offers transformative potential in pathology, where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information. While the rapid development of AI-driven computational pathology tools is revolutionizing disease interpretation, these technologies have not yet been systematically evaluated. Therefore, this review systematically evaluates AI applications across the diagnostic continuum, from image preprocessing and tumor classification to prognostic stratification and the discovery of predictive biomarkers. It presents a technical taxonomy of the algorithms and foundation models powering these applications, benchmarking their performance across diverse diagnostic tasks through rigorous comparative analyses. It also identifies critical challenges in clinical translation, including computational scaling, noisy annotations, interpretability gaps, and domain shifts. Finally, it proposes a roadmap for advancing AI applications in precision oncology and pathological research. By bridging technological innovation with clinical needs, this review aims to accelerate the integration of robust, unified, scalable AI solutions into diagnostic workflows.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"12 1","pages":"93"},"PeriodicalIF":22.9,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12765299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145896602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Type 2 diabetes does not inevitably shorten life expectancy.","authors":"Huai-Dong Du","doi":"10.1016/j.mmr.2026.100029","DOIUrl":"https://doi.org/10.1016/j.mmr.2026.100029","url":null,"abstract":"","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"13 1","pages":"100029"},"PeriodicalIF":22.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Burden of women's cancers in the group of twenty (G20) from 1990 to 2023: epidemiological trends and impact from fertility, quality of care, and survival.","authors":"Meng-Long Li, Rui-Shu Tang, Nan Wang, Jin-Lei Qi, Hui-Ming He, Meng-Ying Guan, Miao Li, Bing-Qing Wu, Yeerlin Asihaer, Sten H Vermund, Yi-Fei Hu","doi":"10.1016/j.mmr.2026.100026","DOIUrl":"https://doi.org/10.1016/j.mmr.2026.100026","url":null,"abstract":"<p><strong>Background: </strong>Cancer in women represents a significant disease burden, posing challenges for prevention, treatment, and caregiving. This study aimed to analyze the epidemiological trends of the women's cancer burden and the main influencing factors in the group of twenty (G20) from 1990 to 2023.</p><p><strong>Methods: </strong>Incidence, prevalence, mortality, and disability-adjusted life years (DALYs) for breast, cervical, uterine, and ovarian cancers, as well as fertility rates for G20 and its 98 locations, were sourced from the Global Burden of Disease Study 2023. Age-standardized rates (ASRs), quality of care index (QCI), and 5-year relative survival of integrated women's cancers were calculated. Average annual percent changes (AAPCs) were used to determine the temporal trends by age and region. Decomposition analysis identified drivers of changes in case numbers, linear regression assessed the associations with DALY rate changes, and dominance analysis identified dominant predictors.</p><p><strong>Results: </strong>In 2023, the incidence, prevalence, mortality, and DALYs from women's cancers in G20 were 3.29 [95% uncertainty interval (UI) 2.60-4.14], 26.71 (95% UI 21.99-32.40), 1.16 (95% UI 0.91-1.45), and 36.58 million (95% UI 28.40-46.32), respectively, with ASRs of 87.63/100,000 (95% UI 65.12-115.85), 706.16/100,000 (95% UI 555.75-890.02), 30.03/100,000 (95% UI 22.10-39.58), and 994.79/100,000 (95% UI 728.43-1328.81). The QCI was 75.13 [95% confidence interval (CI) 73.67-76.59], and the 5-year relative survival was 65.74% (95% CI 65.53-65.95). From 1990 to 2023, there was a significant increase in incidence, prevalence, mortality, and DALYs in G20, primarily driven by population growth. Age-standardized incidence rate, QCI, and 5-year relative survival increased, while age-standardized mortality and DALY rates decreased. Changes in prevalence rates of breast cancer and cervical cancer for women aged 15-49 years were positively associated with changes in DALY rates of women's cancers, whereas changes in the total fertility rate were negatively associated. Dominance analysis confirmed these three factors consistently as dominant predictors between 1990 and 2023. Reducing the prevalence of breast and cervical cancers and increasing fertility among women aged 15-49 years could lower the overall DALY burden attributable to women's cancer.</p><p><strong>Conclusions: </strong>The incidence, prevalence, mortality, and DALYs of women's cancers in G20 have increased substantially from 1990 to 2023. Tailored prevention strategies should consider age and cancer type, emphasizing reproductive health for women of reproductive age.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"13 1","pages":"100026"},"PeriodicalIF":22.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endothelial barrier and sepsis: mechanisms and potential therapeutic strategies.","authors":"Yan-Hong Jiang, Xing-Juan Li, De-Cao Ma, Yong-Lin Chen, Yi-Yu Shi, Yang Lu, Ren-Fang Mao","doi":"10.1016/j.mmr.2026.100013","DOIUrl":"https://doi.org/10.1016/j.mmr.2026.100013","url":null,"abstract":"<p><p>Sepsis is a life-threatening condition characterized by an exaggerated and uncontrolled immune response, leading to widespread inflammation throughout the body. This immune response can compromise the integrity of the endothelial barrier, resulting in increased vascular permeability. The degree of vascular permeability in septic shock patients correlates with disease severity and significantly influences the outcomes of resuscitation and prognosis. This review systematically examines the structural regulation of the endothelial barrier and the dynamic mechanisms of its injury in subgroups of sepsis. In response to these mechanisms, emerging therapeutic strategies focus on glycocalyx protection, signal pathway modulation, cytoskeleton stability, and immune regulation, aiming to restore endothelial barrier function through multi-target synergism. In the future, combining analysis of endothelial barrier function and the dynamic regulation mechanism provides a new perspective for the precise treatment of sepsis.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"13 1","pages":"100013"},"PeriodicalIF":22.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13127147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.","authors":"Cheng-Kun Cao, Xin-Yi Xu, Fei Liang, Min Yao, Yuan-Yuan Chen, Xiao-Kun Li, Zhi-Jian Su","doi":"10.1016/j.mmr.2026.100009","DOIUrl":"https://doi.org/10.1016/j.mmr.2026.100009","url":null,"abstract":"<p><p>Nanobodies (Nbs), the antigen-binding single-domain fragments derived from camelid heavy-chain antibodies (Abs), have rapidly become a focus of biomedical research due to their compact size, high stability, strong antigen affinity, and ease of molecular engineering. This review systematically outlines their structural and functional features, current strategies for acquisition, screening, optimization, and large-scale production, and comprehensively discusses their wide-ranging applications in therapeutics, diagnostics, and basic research. Specifically, Nbs have shown outstanding efficacy in tumor, toxin, infectious, and cardiovascular disease treatments, while serving as versatile tools for molecular imaging, biosensing, protein purification, structural analysis, and intracellular regulation. The challenges of immunogenicity, off-target effects, and industrial-scale manufacturing are also critically examined. Furthermore, the integration of artificial intelligence in structure prediction, de novo design, and immunogenicity assessment has opened powerful new avenues for rational Nb engineering. Combined with emerging technologies such as gene therapy, nanomaterial delivery, and multispecific architectures, these advances promise to accelerate clinical translation. Overall, Nb technology is poised to become a cornerstone of next-generation precision medicine and biotechnology, offering innovative solutions for disease diagnosis, targeted therapy, and molecular discovery.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"13 1","pages":"100009"},"PeriodicalIF":22.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13127154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salivary biomarkers in oral cancer diagnosis: advancing conventional treatment strategies.","authors":"Ankit Paul, Tarun Kumar Upadhyay, Adil Ali, Hemant Singh, Mohd Saeed, Farrukh Aqil","doi":"10.1016/j.mmr.2026.100018","DOIUrl":"https://doi.org/10.1016/j.mmr.2026.100018","url":null,"abstract":"<p><p>Oral squamous cell carcinoma (OSCC) is a major global health challenge, with most cases being diagnosed at advanced stages. Traditional diagnostic methods are often invasive and costly, and can delay diagnosis. Saliva has emerged as a promising non-invasive source of biomarkers for OSCC detection. This highlights the need for accessible, non-invasive, and sensitive biomarkers for OSCC detection. This review critically evaluates the current status and future potential of salivary biomarkers in OSCC, with an emphasis on their diagnostic efficacy, sensitivity, specificity, clinical validation, and advantages over traditional serum- and plasma-based markers. Saliva is a promising liquid for biopsy due to its non-invasive collection and molecular richness. We summarize evidence on diverse salivary biomarkers, including microRNAs (miRNAs), proteins, metabolites, circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA), highlighting their dysregulation in OSCC and diagnostic utility. Particular emphasis is placed on CTCs, ctDNA, and miRNAs, which demonstrate stability in saliva and potential for early detection. We further discuss advances in next-generation sequencing, mass spectrometry, and artificial intelligence/machine learning that enable the development of biomarker panels with improved diagnostic accuracy over single markers. Despite challenges such as sample heterogeneity and the lack of standardized protocols, salivary biomarkers hold strong potential to transform OSCC care by enabling earlier detection, guiding personalized therapies, and supporting non-invasive disease monitoring. However, achieving methodological standardization, validating biomarkers across diverse cohorts, and integrating them into clinical workflows are imperative before their routine application in practice.</p>","PeriodicalId":18581,"journal":{"name":"Military Medical Research","volume":"13 1","pages":"100018"},"PeriodicalIF":22.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13127156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}