RESUMO
Exploiting the chirality of nanometric structures to modulate biological systems is an emerging and compelling area of research. In this study, we reveal that chiral polyurea nanocapsules exhibit significant stereoselective interactions with albumins from various sources despite their nearly neutral surface potential. Moreover, these interactions can be modulated by altering the nanocapsule surface composition, offering new opportunities to impact their distribution and, if used as a drug delivery system, the pharmacokinetics of the drug. Notably, these interactions promote preferential cellular internalization of only one chiral configuration. We synthesized chiral polyurea nanocapsules with reproducible sizes via interfacial polymerization between toluene 2,4-diisocyanate and d- or l-lysine enantiomers on a volatile oil-in-water emulsion interface, followed by solvent evaporation. Further synthesis optimization reduced the capsule size to a range compatible with in vivo administration, and capsules with alternating chiral patterns were also produced. The stereoselective interactions with albumins were assessed through capsule size changes, fluorescence quenching, and surface charge measurements. Biocompatibility, stability, and cellular internalization were evaluated. Additionally, scanning transmission electron and atomic force microscopy were carried out to assess the capsule shape, surface composition, and morphology. We discovered that d-nanocapsules exhibited 2.1-2.6 times greater albumin adsorption compared with their l-counterparts. This difference is attributed to the distinct morphology of d-nanocapsules, characterized by a more concave shape, central depression, and rougher surface. The extent of adsorption could be finely tuned by adjusting the d- and l-lysine monomer ratios during synthesis. Both chiral configurations demonstrated biocompatibility and stability with d-nanocapsules showing a 2.5-fold increase in cellular internalization.
RESUMO
OBJECTIVES: Adipose tissue radiodensity in computed tomography (CT) performed before surgeries can predict surgical difficulty. Despite its clinical importance, little is known about what influences radiodensity. This study combines desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and electrospray ionization (ESI) with machine learning to unveil how chemical composition of adipose tissue determines its radiodensity. METHODS: Patients in the study underwent abdominal surgeries. Before surgery, CT radiodensity of fat near operated sites was measured. Fifty-three fat samples were collected and analyzed by DESI-MSI, ESI, and histology, and then sorted by radiodensity, demographic parameters, and adipocyte size. A non-negative matrix factorization (NMF) algorithm was developed to differentiate between high and low radiodensities. RESULTS: No associations between radiodensity and patient age, gender, weight, height, or fat origin were found. Body mass index showed negative correlation with radiodensity. A substantial difference in chemical composition between adipose tissues of high and low radiodensities was observed. More radiodense tissues exhibited greater abundance of high molecular weight species, such as phospholipids of various types, ceramides, cholesterol esters and diglycerides, and about 70% smaller adipocyte size. Less radiodense tissue showed high abundance of short acyl-tail fatty acids. CONCLUSIONS: This study unveils the connection between abdominal adipose tissue radiodensity and its chemical composition. Because the radiodensity of the fat around the surgical site is associated with surgical difficulty, it is important to understand how adipose tissue composition affects this parameter. We conclude that fat tissue with a higher content of various phospholipids and waxy lipids is more CT radiodense. CLINICAL RELEVANCE STATEMENT: This study establishes the connection between the CT radiodensity of adipose tissue and its chemical composition. Clinicians may use this information for preoperative planning of surgical procedures, potentially modifying their surgical approach (for example, performing partial nephrectomy openly rather than laparoscopically). KEY POINTS: ⢠Adipose tissue radiodensity values in computed tomography images taken prior to the surgery can potentially predict surgery difficulty. ⢠Fifty-three human specimens were analyzed by advanced mass spectrometry, molecular imaging, and machine learning to establish the key features that determine Hounsfield units' values of adipose tissue. ⢠The findings of this research will enable clinicians to better prepare for surgical procedures and select operative strategies.