mAbs最新文献

{"title":"Deep learning assessment of nativeness and pairing likelihood for antibody and nanobody design with AbNatiV2.","authors":"Aubin Ramon, Niccolò Frassetto, Haowen Zhao, Xing Xu, Matthew Greenig, Shimobi Onuoha, Pietro Sormanni","doi":"10.1080/19420862.2026.2646361","DOIUrl":"10.1080/19420862.2026.2646361","url":null,"abstract":"<p><p>Most immune-system created antibodies balance good binding and stability with low toxicity and self-reactivity. Quantifying the nativeness of a candidate sequence - its likelihood of belonging to natural immune repertoires - has thus emerged as a valuable strategy for hit selection from synthetic libraries, optimization and humanization, and for guiding de novo design toward developable candidates. We previously introduced AbNatiV, a transformer-based VQ-VAE for nativeness assessment, which proved effective across multiple nanobody engineering tasks. However, AbNatiV1 operated on unpaired sequences, limiting applicability to conventional VH-VL antibodies. Moreover, its performance on nanobody nativeness was constrained by the limited number and diversity of nanobody repertoires available at the time. Here, we sequenced new camelid repertoires, curated additional recent datasets, and present AbNatiV2: an enhanced architecture comprising various models each trained on ≥20 million sequences. AbNatiV2 improves nanobody nativeness classification across held-out and diverse test sets, and more robustly detects nativeness changes upon CDR grafting. We also introduce p-AbNatiV2, a cross-attention model fine-tuned on 3.7 million paired human sequences. p-AbNatiV2 provides residue- and sequence-level humanness for VH/VL pairs and learns pairing-likelihood via noise-contrastive training. On held-out tests, it assigns the native pair a higher score in 74% of cases, substantially outperforming recent pairing models. Together, AbNatiV2 and p-AbNatiV2 extend nativeness assessment and engineering to both nanobodies and conventional antibodies, supporting design decisions at single-residue, Fv-sequence, and paired-domain levels. AbNatiV2 is available as downloadable software and webserver.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2646361"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models.","authors":"Marc Hoffstedt, Jannis Wowra, Hermann Wätzig, Knut Baumann","doi":"10.1080/19420862.2026.2644655","DOIUrl":"10.1080/19420862.2026.2644655","url":null,"abstract":"<p><p>Many complex models for antibody affinity prediction have been developed and successfully deployed. Recent results for T-cell receptor epitope prediction have shown, that even simple distance-based models can achieve a similar performance while requiring less parameters, being more easily interpretable and faster to compute. Encouraged by these results AbDist, a new distance-based model, was developed for antibody affinity prediction. It uses fragments around mutation sites to calculate distances between antibody sequences, demonstrating that a local environment alone suffices as an effective featurization. AbDist was used to perform classification and regression tasks on multiple disjunct public datasets. Its performance matches state-of-the-art machine-learning (ML) models. AbDist is interpretable, computationally efficient, and well suited for data-sparse, early-stage antibody engineering workflows, while sharing the limited out-of-distribution generalization common to current models. AbDist is available as an open-source, publicly accessible tool.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2644655"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147474409","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":"A comprehensive evaluation of the human Tg32 and Tg276 FcRn transgenic mouse and non-human primate models for predicting antibody pharmacokinetics in human.","authors":"Monika Gajewska, Michael Kiffe, Claudio Calonder, Volker Engelhardt, Carla Pou I Miralbell, Elena Gonzalo-Gil, Gregory Christianson, Nijaguna Bethur, Adriano Flora","doi":"10.1080/19420862.2026.2668888","DOIUrl":"https://doi.org/10.1080/19420862.2026.2668888","url":null,"abstract":"<p><p>In this study, we present a comprehensive evaluation of two human FcRn transgenic mouse models, Tg276 and Tg32, demonstrating their ability to predict the pharmacokinetic (PK) parameters of IgG-type antibodies in human and monkey (non-human primate, NHP), including molecules with and without half-life extension. To assess the translational relevance of the humanized FcRn mouse models, we integrated a broad dataset comprising <i>in vivo</i> PK parameters in both animals and humans derived from the literature and in-house experiments. Using this dataset, we optimized scaling exponents, performed allometric scaling, and evaluated the predictive performance of the models. The optimized exponents fell within expected ranges, 0.7 to 0.95 for systemic and intercompartmental clearance, and around 1 for central and peripheral volumes of distribution. Our analysis reveals the need to apply distinct scaling exponents for half-life extended versus non-extended antibodies, as well as model-specific exponents. The results demonstrate that human PK predictions from Tg32 and Tg276 mice are comparable to those from the NHP. These models therefore offer a good alternative to the monkey, potentially reducing the need to conduct early <i>in vivo</i> PK studies in NHPs. We also report for the first time the use of an immunocompromised version of the Tg276 model to mitigate anti-drug antibody responses. Our findings show that the Tg276 hemizygous model exhibits a translational performance equal to, and in some cases superior to, that of the Tg32 homozygous strain and the NHP, specifically, in terms of predicted clearance. These insights support the Tg276 model as a valuable tool for early-stage antibody screening and lead optimization in preclinical PK evaluation.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2668888"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid and selective characterization of antibody-drug conjugates in complex sample matrices by native affinity liquid chromatography-mass spectrometry.","authors":"Dan Bach Kristensen, Nanna Sofie Eskesen, Clara Coll-Satue, Alexandre Nicolas, Jan Kirkeby Simonsen, Lykke Rasmussen, Trine Meiborg Sloth, Martin Ørgaard, Elizabeta Madzharova, Simon Krabbe, Katrine Zinck Leth, Pernille Foged Jensen, Alain Beck","doi":"10.1080/19420862.2026.2618314","DOIUrl":"10.1080/19420862.2026.2618314","url":null,"abstract":"<p><p>Antibody-drug conjugates (ADCs) and other biopharmaceuticals require robust analytical methods to assess biotransformation in biological matrices. Current approaches often require off-line enrichment and extensive chromatographic separation, limiting throughput and complicating data processing. We developed a native affinity liquid chromatography-mass spectrometry (aLC-MS) method using POROS CaptureSelect FcXL columns combined with optimized solvents and MS parameters for direct analysis (1D aLC-MS) of ADCs and other antibody-derived formats in complex sample matrices, such as serum. The method was evaluated using stability studies and concentration series in mouse serum. Direct analysis enabled accurate determination of drug-antibody ratio (DAR), drug-load distribution (DLD) and relative drug abundance across samples without chromatographic peak integration. Stability studies revealed distinct ADC biotransformation profiles in serum versus PBS, including maleimide hydrolysis and disulfide exchange at under-conjugated cysteine sites. The aLC-MS method achieved excellent linearity (R² = 0.99) over 125-2000 µg/mL in serum and demonstrated sensitivity to 31.25 µg/mL. This rapid, selective aLC-MS method enables high-throughput monitoring of ADC quality attributes in complex matrices with minimal sample preparation, supporting biopharmaceutical product development and bioanalysis applications. The method is exclusively based on MS results, which makes data processing and reporting fast and easy to automate.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2618314"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12818810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994360","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":"<i>Alpseq</i>: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing.","authors":"Kathleen Zeglinski, Jakob Schuster, Jaison D Sa, Amy Adair, Jing Deng, Phillip Pymm, Matthew E Ritchie, Rory Bowden, Wai-Hong Tham, Quentin Gouil","doi":"10.1080/19420862.2026.2623326","DOIUrl":"10.1080/19420862.2026.2623326","url":null,"abstract":"<p><p>Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns; however, the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed <i>alpseq</i>, an optimized, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. <i>alpseq</i> is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The <i>alpseq</i> software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. <i>alpseq</i> also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. <i>alpseq</i> thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. <i>alspeq</i> is available at https://github.com/kzeglinski/alpseq.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2623326"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146106112","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":"Mannose receptor contributes to preferential oligomannosidic glycoform clearance in a murine pharmacokinetics model.","authors":"David Falck, Steinar Gijze, Anna M Wasynczuk, Sebastian Malik, Aline Tridon, Martin Lechmann, Manfred Wuhrer, Dietmar Reusch","doi":"10.1080/19420862.2026.2652119","DOIUrl":"10.1080/19420862.2026.2652119","url":null,"abstract":"<p><p>The pharmacokinetic profile of therapeutic antibodies, which affects the dosing frequency, efficacy, and cost of the products, is influenced by several molecular features, including N- glycosylation. While the accelerated clearance of antibodies carrying non-complex type glycans is well established, the mechanism behind this phenomenon is poorly understood. Though there have been indirect indications before, we provide the first direct evidence for the involvement of the mannose receptor in the clearance of therapeutic antibodies. Wild-type (WT) mice showed accelerated clearance of an oligomannose enriched antibody, and this effect was reduced in mannose receptor (MRC1) knockout (KO) mice. Subsequent glycoform-resolved analysis by mass spectrometry showed in WT an accelerated clearance of oligomannosidic compared to complex glycoforms. This accelerated clearance was also observed in MRC1 KO mice, but to a lesser extent. Interestingly, the glycoform distribution of total circulating mouse antibodies, which do not contain oligomannosidic glycoforms, was not affected by MRC1 KO. Hence, we show that the mannose receptor is involved in, but not exclusively responsible for, preferential clearance of oligomannosidic glycoforms of antibodies. This points toward a role for additional, glycan-targeting receptors for therapeutic antibody clearance. In addition, we observed a preferential clearance of complex afucosylated glycoforms, unique to the mouse model, which was MRC1-independent.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2652119"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147581653","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":"Targeting IL-7Rα with PNU-159682 antibody-drug conjugates in acute lymphoblastic leukemia: translational implications.","authors":"Shiqi Yang, Takahiro Anzai, Ryo Tsumura, Hirobumi Fuchigami, Motochika Hamada, Junichiro Yuda, Koichi Ikuta, Masahiro Yasunaga","doi":"10.1080/19420862.2026.2663639","DOIUrl":"10.1080/19420862.2026.2663639","url":null,"abstract":"<p><p>Relapsed acute lymphoblastic leukemia (ALL), particularly with central nervous system (CNS) involvement, remains a major cause of treatment failure and is inadequately controlled by existing antibody-drug conjugates (ADCs) with tubulin inhibitors. To address this limitation, we developed IL-7Rα-targeted monoclonal antibodies and identified clone 577 as the lead candidate. Using this antibody, we generated ADCs conjugated with either monomethyl auristatin E (MMAE) or the highly potent DNA-damaging payload-PNU-159682 (PNU). In head-to-head comparisons, 577-PNU showed >50-fold greater potency than 577-MMAE in vitro and induced complete tumor regression in xenografts at a 20-fold lower dose. Additionally, 577-PNU provided durable systemic disease control and markedly reduced leukemic infiltration in the brain and spinal cord in both preventive and established murine CNS disease models, offering direct evidence of effective CNS penetration. Safety assessments demonstrated stable body weight, normal hematology and serum biochemistry, and no treatment-related pathologies. Collectively, these findings provide the first preclinical evidence that IL-7Rα-directed ADCs armed with DNA-targeting payload PNU-159682 can achieve durable elimination of systemic and CNS leukemia at tolerable doses, demonstrating both clinical feasibility and CNS disease control, and establishing a compelling rationale for their translational and clinical development in relapsed and refractory ALL.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2663639"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775773","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":"Impact of process parameters on IgG glycosylation in CHO systems: a comprehensive quantitative analysis.","authors":"Javier Bravo-Venegas, Jose Rodriguez-Siza, Mauricio Vergara, Mauro Torres, Alan Dickson, Jorge R Toledo, María Carmen Molina, Marcela A Hermoso, Julio Berríos, Claudia Altamirano","doi":"10.1080/19420862.2026.2643039","DOIUrl":"10.1080/19420862.2026.2643039","url":null,"abstract":"<p><p>Controlling glycosylation, a critical quality attribute of biopharmaceuticals such as monoclonal antibodies, is essential, as it significantly influences biological activity and therapeutic efficacy. Although numerous studies have examined the impact of process parameters (PP, e.g. temperature, pH, dissolved oxygen) on glycosylation, the lack of standardized reporting makes cross-study comparisons challenging and prevents clear conclusions. Here, we systematically reviewed the literature and applied a normalized quantitative framework, the Glycan Indices approach, as a standardized quantitative criterion to evaluate the impact of process parameters on glycoform distribution in IgG-producing CHO cell systems objectively. This methodology enabled the integration and reinterpretation of large, heterogeneous datasets, validating some well-known patterns while providing novel perspectives about process parameters. Our analysis revealed that PP manipulations of pH, dissolved oxygen or CO₂ partial pressure rarely resulted in meaningful shifts in glycosylation, with changes <5% observed for galactose, fucose, or N-acetylneuraminic acid content. In contrast, for several cases temperature and osmolality changes notably affected galactosylation (>10%) and fucosylation (1-10%), variations that may have significant biological consequences. To our knowledge, this is the first comprehensive quantitative assessment of process parameters effects on glycosylation, showing that such influences are consistently limited, independent of CHO cell line or culture mode. Based in our observations we strongly recommend reporting both glycan distribution and glycan indices when performing glycan analysis. Dual reporting facilitates inter-study comparisons and prevents subtle shifts in sugar moieties from being masked by glycan redistribution.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2643039"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12990948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147463695","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":"Balancing the extremes for antibody developability: hydrophobic and electrostatic germline framework signatures for CDR-loop compensation.","authors":"Vera A Spanke, Valentin J Egger-Hoerschinger, Clarissa A Seidler, Katharina B Kroell, Vincent Wieser, Sabine Imhof-Jung, Benjamin Weiche, Alexander Bujotzek, Guy Georges, Klaus R Liedl","doi":"10.1080/19420862.2026.2627669","DOIUrl":"10.1080/19420862.2026.2627669","url":null,"abstract":"<p><p>Antibody therapeutics are a rapidly growing class of biopharmaceuticals, but concerns regarding potential developability issues persist. While complementarity-determining region (CDR) loops are imperative for antigen specificity and mutations are challenging, the framework regions can be exchanged to align with developability attributes such as aggregation, clearance, and viscosity, all governed by physicochemical characteristics. In this study, we systematically analyze the electrostatic and hydrophobic surface properties of germline-encoded antibody frameworks to assess their role in modulating Fv developability. Using structure prediction and surface patch analysis, we identify differences between kappa and lambda light-chain frameworks, characterize outlier germlines with extreme surface properties, and demonstrate using hydrophobic interaction chromatography and a heparin column that framework selection can compensate for CDR loop physicochemical characteristics. Our findings reveal that rational framework selection can serve as a systematic tool for optimizing antibody developability. This study provides a toolbox for antibody design, enhancing therapeutic candidate selection by leveraging inherent germline properties.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2627669"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12915817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146180714","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":"Tokenizing loops of antibodies.","authors":"Ada Fang, Robert G Alberstein, Simon Kelow, Frédéric A Dreyer","doi":"10.1080/19420862.2026.2645271","DOIUrl":"10.1080/19420862.2026.2645271","url":null,"abstract":"<p><p>The complementarity-determining regions (CDRs) of antibodies are loop structures that are key to their interactions with antigens and are of high importance to the design of novel biologics. Existing approaches for characterizing the diversity of CDRs have limited coverage and cannot be readily incorporated into protein foundation models. Here we introduce ImmunoGlobulin LOOp Tokenizer, Igloo, a multimodal antibody loop tokenizer that encodes backbone dihedral angles and sequence. Igloo is trained using a contrastive learning objective to map loops with similar backbone dihedral angles closer together in latent space. Compared to state-of-the-art protein encoding approaches, Igloo can efficiently retrieve the closest matching loop structures from a structural antibody database, outperforming the existing methods on identifying similar H3 loops by 6.1%. Igloo assigns tokens to all loops, addressing the limited coverage issue of canonical clusters, while retaining the ability to recover canonical loop conformations. To demonstrate the versatility of Igloo tokens, we show that they can be incorporated into protein language models with IglooLM and IglooALM. On predicting binding affinity of heavy chain variants, IglooLM outperforms the base protein language model on 8 out of 10 antibody-antigen targets. Additionally, it is on par with existing state-of-the-art sequence-based and multimodal protein language models, performing comparably to models with <math><mn>7</mn><mo>×</mo></math> more parameters. IglooALM samples antibody loops which are diverse in sequence and more consistent in structure than state-of-the-art antibody inverse folding models. We show that Igloo can rapidly and scalably prioritize functional antibody variants from large mutagenesis libraries, achieving a <math><mn>1.9</mn><mo>×</mo></math> enrichment of experimentally validated HER2 binders in a zero-shot setting. Igloo demonstrates the benefit of introducing multimodal tokens for antibody loops for encoding their diverse landscape, improving protein foundation models, and for antibody CDR design.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2645271"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623193","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}