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  • RESEARCH

    Zi-Mo Chen, Ying-Yu Jiang, Hong-Qiu Gu, Xin-Miao Zhang, Jing Jing, Jing-Lin Mo, Yong Jiang, Li-Ping Liu, Xing-Quan Zhao, Yong-Jun Wang, and Zi-Xiao Li

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100067
    Abstract：Background:Whether infarct volume refines assessment of recurrent ischemic stroke (IS) and intracerebral hemorrhage (ICH) risk following acute IS beyond the National Institutes of Health Stroke Scale (NIHSS) remains uncertain. We compared their performance in ischemic-hemorrhagic risk stratification.Methods:We included 10,545 patients with acute IS from the Third China National Stroke Registry (CNSR-Ⅲ) with diffusion-weighted imaging/apparent diffusion coefficient-derived infarct volume and NIHSS data. Outcomes were 90-day recurrent IS and ICH, analyzed separately. Two reciprocal Cox models were used: one with infarct volume adjusted for NIHSS, and another with NIHSS adjusted for infarct volume. Stratified analyses, restricted cubic splines, and grouped threshold analyses were exploratory. In the GOLDEN BRIDGE Ⅱ study, recurrent IS and all bleeding events were assessed for external consistency.Results:In CNSR-Ⅲ, larger infarct volume was independently associated with recurrent IS [hazard ratio (HR)=1.35,95% confidence interval (CI) 1.25–1.47] and ICH (HR=2.48, 95% CI 2.11–2.91) after NIHSS adjustment, whereas NIHSS lost significance after infarct-volume adjustment (IS: HR=1.05, 95% CI 0.89–1.24; ICH: HR=1.11, 95% CI 0.78–1.58). ICH risk rose around 9.0 ml, with an increased hazard at 9.0–9.9 ml (HR=3.61, 95% CI 1.29–10.07), while IS risk increased without a sharp rise (HR=1.15, 95% CI 0.98–1.35). Patients with NIHSS score ≥4 and infarct volume <9.0 ml had IS risk comparable to NIHSS score ≤3 but potentially lower ICH risk (HR=0.63, 95% CI 0.38–1.02). Infarct volume 9.0–17.9 ml increased ICH risk (HR=1.96, 95% CI 1.06–3.63). Infarct volume was <9.0 ml in 66.7% of NIHSS score 6–15 and 25.4%of NIHSS score ≥16. GOLDEN BRIDGE Ⅱ confirmed principal findings, with infarct volume associated with recurrent IS(HR=1.37, 95% CI 1.25–1.49) and bleeding events (HR=1.53, 95% CI 1.31–1.78).Conclusions:Infarct volume better characterizes post-stroke ischemic-hemorrhagic risk than NIHSS. Prospective validation is required before using infarct volume to guide antiplatelet selection.  
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  • EDITORIAL

    Martin Burtscher, Johannes Burtscher

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100068
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  • COMMENTARY

    Shamas Mohammed

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100069
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  • PERSPECTIVE

    Abhay Pandit

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100070
    Abstract：An implanted medical device is specified in detail through its bulk and surface chemistry, mechanical properties, architecture, degradation kinetics, and surface ligands, whereas the host receiving it is specified hardly at all. Much of what the host presents at that interface is glycan, and glycan-mediated recognition acts through defined receptor families, including the Siglecs, the C-type lectin receptors, the galectins, and the regulators of complement. The host glycome is not fixed. It is assembled combinatorially, varies with age, metabolic state, and inflammation, differs between recipients, and is altered systematically in disease. Those alterations are measurable before implantation and are resolved both regionally and temporally, N-glycosylation differing region by region within the human Parkinsonian nigrostriatum and α-2,6-sialylation rising from 19.5% of the N-glycan pool in healthy intervertebral disc to 23%–24% in degenerated tissue. This Perspective argues that glycosylation should be specified as a component of the biomaterials from which devices are built. Because a diseased glycome is typically altered in several respects at once, a design need not address all of it, and a single motif may be selected where it is quantitatively dominant, mechanistically implicated, and chemically tractable. Three propositions follow: host glycophenotype is a measurable covariate of outcome and a basis for patient stratification; glycan structure, sulfation, and presentation density are specifiable material properties, with sulfation tunable independently of backbone; the analytical methods required to control a glycan attribute are established, so that the obstacle is convention rather than capability.  
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  • RESEARCH

    Yong-Jie Xu, Jian-Feng Xu, Chang-Fa Xia, Yu-Jie Wu, Dian-Ge Li, Tie-Yan Liu, Huai-Ping Cui, Jin Zhang, Liang Shang, Wan-Qing Chen

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100066
    Abstract：Background:Gastric cancer remains a leading cause of cancer-related mortality worldwide, frequently diagnosed at advanced stages due to the limitations of current diagnostic approaches. While cell-free DNA (cfDNA)-based epigenetic profiling has emerged as a promising avenue for early cancer detection, comprehensive investigations into the epigenetic landscape of cfDNA in gastric cancer remain scarce. To address this gap, we aimed to develop and validate a multimodal epigenetic blood test for the non-invasive detection of gastric cancer.Methods:We developed GastroAlert, a multimodal epigenetic blood test that integrates cfDNA methylation, nucleosome footprinting, and fragmentation features into a single diagnostic approach for gastric cancer detection. A custom-designed capture probe panel was utilized to target gastric cancer-associated differentially methylated CpG sites and functionally relevant genes. Targeted enzymatic methylation sequencing was then performed on a discovery dataset consisting of 136 participants, including 53 gastric cancer patients, 55 individuals with benign gastric conditions, and 28 healthy controls. Subsequently, the diagnostic performance of GastroAlert was rigorously validated in an independent external dataset of 149 participants, comprising 79 gastric cancer cases and 70 non-cancer controls.Results:In cross-validation of the discovery dataset, GastroAlert achieved an area under the receiver operating characteristic curve (AUC) of 0.950 [95% confidence interval (CI) 0.874-0.995], with observed AUCs for conventional protein biomarkers ranging from 0.507 to 0.687. Notably, the locked GastroAlert model exhibited robust and reproducible performance in the independent validation dataset, yielding an AUC of 0.965 (95% CI 0.940-0.989) for distinguishing gastric cancer patients from non-cancer controls. For the clinically critical subset of early-stage gastric cancer cases, the model still maintained high diagnostic efficacy with an AUC of 0.921 (95% CI 0.862-0.980) in the discovery dataset and 0.948 (95% CI 0.910-0.985) in the validation dataset. Among all 79 gastric cancer cases in the validation dataset, the model attained an overall sensitivity of 0.898 (95% CI 0.813-0.948) at a specificity of 0.900 in the validation dataset.Conclusions:The findings demonstrate that multimodal epigenetic analysis of cfDNA provides a robust, non-invasive strategy for early gastric cancer detection, with potential clinical utility to improve patient outcomes.  
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  • LETTER TO THE EDITOR

    Rabea Dettmer, Alice Rovai, Willem Wolkers, Rainer Blasczyk, Constanca Figueiredo

    Corrected Proof
    (2026) DOI: 10.1016/j.mmr.2026.100057
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