Biomedical subjects

Can Chen

Publications and source records attributed to Can Chen.

5 recordsLinked to original sources

PubMed2026

Early life sugar rationing and ageing related diseases, biological ageing and mortality.

Early-life nutrition may influence lifelong ageing, yet human evidence is scarce. Using Britain's postwar sugar rationing as a natural experiment, we examine its long-term effects in 64,809 United Kingdom Biobank participants. Exposure to sugar rationing during the first 1,000 days of life is associated with a 9% lower incidence of hallmark-related disease, with a hazard ratio of 0.91 and a 95% confidence interval of 0.88-0.94, and a 19% lower risk of all-cause mortality, with a hazard ratio of 0.81 and a 95% confidence interval of 0.69-0.93. Mediation analysis indicates that the survival association is statistically mediated, by approximately 60%, through differences in incident hallmark-related disease. Rationed individuals show 1.0-1.2-year younger biological ages across multiple clocks and lower organ ages, particularly in the lung, heart, and liver. Proteomic profiling identifies 47 altered proteins, with enrichment of adenosine monophosphate-activated protein kinase and longevity pathways and suppression of mechanistic target of rapamycin signaling. These findings are consistent with international recommendations to limit free or added sugars from the World Health Organization, United States Dietary Guidelines, and American Heart Association, and may inform policy discussions related to sugar taxation and infant food and marketing policies under the United Nations 2030 Agenda.

Humans↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗

PubMed2006

Influence of metal ionic characteristics on their biosorption capacity by Saccharomyces cerevisiae.

The influence of metal ionic characteristics on their biosorption capacity was analyzed using quantitative structure-activity relationships model. The waste biomass of Saccharomyces cerevisiae was used as biosorbent to adsorb 10 kinds of metal ions, and their maximum biosorption capacity (q (max)) was determined by the Langmuir isotherm model. The values of q (max) decreased in the following order (in millimole per gram): Pb(2+) (0.413) > Ag(+) (0.385) > Cr(3+) (0.247) > Cu(2+) (0.161) > Zn(2+) (0.148) > Cd(2+) (0.137) > Co(2+) (0.128) > Sr(2+) (0.114) > Ni(2+) (0.108) > Cs(+) (0.092). Twenty-two parameters of physiochemical characteristics of metal ions were selected and correlated with q (max), i.e., OX, AN, r (Angstroms), DeltaIP (eV), DeltaE (0) (V), X (m), |log K (OH)|, X(m)(2)(r), Z*(2)/r, AN/DeltaIP, sigma rho, AR, AW, IP, AR/AW, Z/r (2), Z/AR(2), Z/r, Z/AR, Z*(2)/r., Z*, N. The linear regression analysis showed that the covalent index [Formula: see text] was correlated well with q (max) for all metal ions tested in the following equation: q (max) = 0.029 + 0.061 (X(m)(2)r) (R (2) = 0.70). It suggested that the greater the covalent index value of metal ion was, the greater the potential to form covalent bonds with biological ligands, such as sulphydryl, amino, carboxyl, hydroxyl groups, etc. on the biomass surface, and the higher the metal ion biosorption capacity was. Classification of metal ions, for divalent ion or for soft-hard ion could improve the linear relationship (R (2) = 0.89). The equation could be used to predict the biosorption capacity of metal ions.

Adsorption↗

PubMed2006

Biosorption of heavy metals by Saccharomyces cerevisiae: a review.

Heavy metal pollution has become one of the most serious environmental problems today. Biosorption, using biomaterials such as bacteria, fungi, yeast and algae, is regarded as a cost-effective biotechnology for the treatment of high volume and low concentration complex wastewaters containing heavy metal(s) in the order of 1 to 100 mg/L. Among the promising biosorbents for heavy metal removal which have been researched during the past decades, Saccharomyces cerevisiae has received increasing attention due to the unique nature in spite of its mediocre capacity for metal uptake compared with other fungi. S. cerevisiae is widely used in food and beverage production, is easily cultivated using cheap media, is also a by-product in large quantity as a waste of the fermentation industry, and is easily manipulated at molecular level. The state of the art in the field of biosorption of heavy metals by S. cerevisiae not only in China, but also worldwide, is reviewed in this paper, based on a substantial number of relevant references published recently on the background of biosorption achievements and development. Characteristics of S. cerevisiae in heavy metal biosorption are extensively discussed. The yeast can be studied in various forms for different purposes. Metal-binding capacity for various heavy metals by S. cerevisiae under different conditions is compared. Lead and uranium, for instances, could be removed from dilute solutions more effectively in comparison with other metals. The yeast biosorption largely depends on parameters such as pH, the ratio of the initial metal ion and initial biomass concentration, culture conditions, presence of various ligands and competitive metal ions in solution and to a limited extent on temperature. An assessment of the isotherm equilibrium model, as well as kinetics was performed. The mechanisms of biosorption are understood only to a limited extent. Elucidation of the mechanism of metal uptake is a real challenge in the field of biosorption. Various mechanism assumptions of metal uptake by S. cerevisiae are summarized.

Environmental Pollutants↗

PubMed2005

Synthesis and insecticidal activities of novel oxime ether pyrethroids.

A series of novel 2-methylthio-3'/4'-substituted acetophenone oxime O-ethers were synthesized by the reaction of the corresponding acetophenone oximes with halides of pyrethroid alcohols in the presence of sodium hydroxide and phase-transfer catalysis or with triethyl quaternary ammonium salts of halides of pyrethroid alcohols in the presence of sodium hydroxide. These compounds showed notable insecticidal activity against Homopteran and Lepidopteran pests. 2-Methylthio-4'-fluoroacetophenone oxime O-[(2-methylbiphenyl-3-yl)methyl] ether was more effective than the commercial insecticides chlorfenapyr and fenvalerate.

Animals↗