Liquid chromatography−mass spectrometry (LC-MS)based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.
Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.
In this work, we demonstrate the significance of defined surface chemistry in synthesizing luminescent carbon nanomaterials (LCN) with the capability to perform dual functions (i.e., diagnostic imaging and therapy). The surface chemistry of LCN has been tailored to achieve two different varieties: one that has a thermoresponsive polymer and aids in the controlled delivery of drugs, and the other that has fluorescence emission both in the visible and near-infrared (NIR) region and can be explored for advanced diagnostic modes. Although these particles are synthesized using simple, yet scalable hydrothermal methods, they exhibit remarkable stability, photoluminescence and biocompatibility. The photoluminescence properties of these materials are tunable through careful choice of surface-passivating agents and can be exploited for both visible and NIR imaging. Here the synthetic strategy demonstrates the possibility to incorporate a potent antimetastatic agent for inhibiting melanomas in vitro. Since both particles are Raman active, their dispersion on skin surface is reported with Raman imaging and utilizing photoluminescence, their depth penetration is analysed using fluorescence 3D imaging. Our results indicate a new generation of tunable carbon-based probes for diagnosis, therapy or both.
A plethora of nanoarchitectures have been evaluated preclincially for applications in early detection and treatment of diseases at molecular and cellular levels resulted in limited success of their clinical translation. It is important to identify the factors that directly or indirectly affect their use in human. We bring a fundamental understanding of how to adjust the biocompatibility of carbon based spherical nanoparticles (CNPs) through defined chemistry and a vigilant choice of surface functionalities. CNPs of various size are designed by tweaking size (2–250 nm), surface chemistries (positive, or negatively charged), molecular chemistries (linear, dendritic, hyperbranched) and the molecular weight of the coating agents (MW 400–20 kDa). A combination of in vitro assays as tools were performed to determine the critical parameters that may trigger toxicity. Results indicated that hydrodynamic sizes are potentially not a risk factor for triggering cellular and systemic toxicity, whereas the presence of a highly positive surface charge and increasing molecular weight enhance the chance of inducing complement activation. Bare and carboxyl-terminated CNPs did present some toxicity at the cellular level which, however, is not comparable to those caused by positively charged CNPs. Similarly, negatively charged CNPs with hydroxyl and carboxylic functionalities did not cause any hemolysis.
The enzyme adenine DNA glycosylase, also called MutY, is known to catalyze base excision repair by removal of adenine from the abnormal 2'-deoxyadenosine:8-oxo-2'-deoxyguanosine pair in DNA. The active site of the enzyme was considered to consist of a glutamic acid residue along with two water molecules. The relevant reaction mechanism involving different barrier energies was studied theoretically. Molecular geometries of the various molecules and complexes involved in the reaction, e.g., the reactant, intermediate, and product complexes as well as transition states, were optimized employing density functional theory at the B3LYP/6-31G(d,p) level in the gas phase. It was followed by single-point energy calculations at the B3LYP/AUG-cc-pVDZ, BHandHLYP/AUG-cc-pVDZ, and MP2/AUG-cc-pVDZ levels in the gas phase. Single-point energy calculations were also carried out at the B3LYP/AUG-cc-pVDZ and BHandHLYP/AUG-cc-pVDZ levels in aqueous media as well as in the solvents chlorobenzene and dichloroethane. For the solvation calculations, the integral equation formalism of the polarizable continuum model (IEF-PCM) was employed. It is found that glutamic acid along with two water molecules would effectively cleave the glycosidic bond of adenosine by a new two-step reaction mechanism proposed here which is different from the three-step mechanism proposed by other authors earlier regarding the working mechanism of MutY.
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