Comprehensive Summary Monitoring and analyzing expression levels of multiple biomarkers in biological samples can improve disease risk prediction and guide precision medicine but suffers from high cost and being time-consuming. Here, we construct a fast molecular classifier based on freeze-thaw cycling that implements an in silico support vector machine (SVM) classifier model at the molecular level with a panel of disease-related biomarkers expression patterns for rapid disease diagnosis. The molecular classifier employs DNA reaction networks as the computing module and repeated dehydration and concentration process as the driving force to implement a set of simplified mathematical operations (such as multiplication, summation and subtraction) for efficient classification of complex input patterns. We demonstrate that the fast DNA-based molecular classifier enables precise cancer diagnosis within a short turnaround time in synthetic samples compared to those of free diffusion classifiers. We envision that this all-in-one molecular classifier will create more opportunities for inexpensive, accurate, and rapid disease diagnosis, prognosis and therapy, particularly in emergency departments or the point of care.
Lin-Lin Cao,Yi Han,Lin Pei,Zhi-Hong Yue,Bo-Yu Liu,Jing-Wen Cui,Mei Jia,Hui Wang
Type 2 diabetes mellitus (T2DM) has been identified as an independent risk factor for hepatocellular cancer (HCC). However, there are no ideal biomarkers for the surveillance and early detection of HCC in the T2DM population at present. In this study, we aimed to explore novel metabolite biomarkers for T2DM-positive [T2DM(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in T2DM(+) HCC patients in untargeted metabolomic analyses. Targeted metabolite analyses confirmed that serum benzoic acid and citrulline were increased, and creatine was decreased in T2DM(+) HCC compared to the T2DM group. A metabolite classifier including benzoic acid, creatine, and citrulline was identified as a novel biomarker for the diagnosis of T2DM(+) HCC, with an area under the ROC curve (AUC) of 0.93 for discriminating T2DM(+) HCC patients from T2DM patients. In addition, the metabolite classifier detected small-size (AUC = 0.94), early-stage (AUC = 0.94), and AFP-negative (AUC = 0.96) tumors with high sensitivity and specificity. The combination of this metabolite classifier and AFP might be useful in the surveillance and early detection of HCC in the T2DM population. In conclusion, this study establishes a novel diagnostic tool for T2DM(+) HCC.Keywords:hepatocellular cancer;type 2 diabetes mellitus;metabolomics;metabolite classifier;benzoic acid;creatine;citrulline
Sheng Huang,Xiangyi Bai,Zhenyu Xu,Li Meng,Shasha Gao,Lei Zhu,Qiuchen Yu,Man-Kay Law
To address the limitations of traditional blood glucose monitoring in industrial environments, this study presents a charge transfer-enhanced glucose sensor based on organic electrochemical transistor, utilizing a carboxymethyl cellulose-modified PEDOT:PSS with aminated multi-walled carbon nanotubes/gold nanoparticles composite film. The sensor is integrated with superabsorbent polymer composites for non-invasive breath analysis. Molecular electrostatic potential calculations reveal that hydrogen-bond networks between carboxymethyl cellulose carboxyl groups and glucose hydroxyl moieties, together with π-π interactions of glucose molecules at the composite surface, enhance interfacial charge transfer efficiency by 2.5 % (0.236e→0.242e). Structural characterization confirms a honeycomb-like porous architecture (0.5–2 μm pore size) formed via carboxymethyl cellulose-mediated hydrogen bonding, accelerating glucose diffusion while suppressing interferent adsorption (response<1 %). The composite's three-dimensional conductive network combines PEDOT:PSS (p-type pathways), carbon nanotubes (surface area expansion) and gold nanoparticles (catalytic sites), achieving a linear detection range of 2–22 μmol·L⁻¹ (extendable up to 9 mM) in exhaled breath condensate with 56.656 μA·(mmol·L⁻¹·cm²)⁻¹ sensitivity, 0.2 μmol·L⁻¹ detection limit (S/N = 23.8), and < 4.8 s response time. The sensor-integrated facemask platform uses superabsorbent polymer to maintain 100 % relative humidity, stabilizing the electrolyte layer for real-time monitoring. Machine learning (random forest classifier processing 18 time-frequency features from transistor outputs) achieves 87.5 % accuracy in resolving glucose fluctuations. This work demonstrates the integration of hierarchical materials, interfacial charge control, and algorithmic processing for wearable biosensors in health monitoring.
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