1c:I[419806,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/30036bedc5269936.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/cd5e572bc3e4eb92.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/d4ac9dcf27f6ff52.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 1d:T432,The study of nanostructured drug delivery systems allows the development of novel platforms for the efficient transport and controlled release of drug molecules in the harsh microenvironment of diseased tissues of living systems, thus offering a wide range of functional nanoplatforms for smart application in biotechnology and nanomedicine. This article highlights recent advances of smart nanocarriers composed of organic (including polymeric micelles and vesicles, liposomes, dendrimers, and hydrogels) and inorganic (including quantum dots, gold and mesoporous silica nanoparticles) materials. Despite the remarkable developments of recent synthetic methodologies, most of all nanocarriers’ action is associated with a number of unwanted side effects that diminish their efficient use in biotechnology and nanomedicine applications. This highlights some critical issues in the design and engineering of nanocarrier systems for biotechnology applications, arising from the complex environment and multiform interactions established within the specific biological media.1e:T4fc,Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MS ⁿ-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS² data, including t_R, MS¹, and MS² information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS² information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS ⁿ-based nontargeted metabolomics study.1f:T61a,

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