Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

Introduction: Cystic fibrosis (CF) predominantly affects young adults. Accurate radiological assessment of pulmonary disease is vital for predicting exacerbations, one of the leading causes of morbidity and mortality. We evaluated the image quality of model-based iterative reconstruction (MBIR) ultra-low-dose CT chest (ULD-CT) in CF evaluation. Methods: We compared ULD-CT with standard adaptive statistical iterative reconstruction (ASIR) low-dose CT (LD-CT). Subjective assessment of contrast and noise were performed for each study. Background noise, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated and compared between the CT studies. Conspicuity of major structures was assessed. These aspects of image quality were compared to determine whether ULD-CT was superior to LD-CT in assessment of CF. Results: The ULD-CT achieved median effective dose of 0.073 mSv, comparable to one standard chest radiograph and significantly lower than the median LD-CT dose of 1.22 mSv. ULD-CT had lower subjective contrast and higher subjective noise when compared to LD-CT. Objectively measured background noise was lower in ULD-CT (16.33 HU vs 38.53 HU, P < 0.0001) compared to LD-CT. ULD-CT had higher median CNR (52.65 vs 22.09, P < 0.0001) and SNR in lung (9.08 vs 7.29, P = 0.002) compared to LD-CT. ULD-CT was equal to LD-CT in identification of trachea, bronchi, pleural and pericardium. Interobserver reliability showed agreement of 80-92%. Conclusions: The image quality of ULD-CT is similar to LD-CT, at 1/16th the dose. MBIR constructed ULD-CT is an effective imaging modality for CF surveillance, with potential applications in other disease settings.

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Solvent-based ambient ionization mass spectrometry (MS) techniques provide a powerful approach for direct chemical analysis and molecular profiling of biological tissues. While molecular profiling of tissues has been widely used for disease diagnosis, little is understood about how the interplay among solvent properties, matrix effects, and ion suppression can influence the detection of biological molecules. Here, we perform a systematic investigation of the extraction processes of lipids using an ambient ionization droplet microsampling platform to investigate how the physicochemical properties of the solvent systems and extraction time influence molecular extraction and detection. Direct molecular profiling and quantitative liquid chromatography–mass spectrometry (LC–MS) of discrete solvent droplets after surface sampling were investigated to provide insights into extraction and ionization mechanisms. The results of this study suggest that intermolecular interactions such as hydrogen bonding play a major role in extraction and detection of lipids using solvent-based ambient ionization techniques. In addition, extraction time was observed to impact the molecular profiles obtained, suggesting optimization of this parameter can be performed to favor detection of specific analytes.

A novel approach for the determination of parts-per-billion level of 5-hydroxymethyl-2-furaldehyde, furfuryl alcohol, furfural, 2-furyl methyl ketone, and 5-methylfurfural in transformer or rectifier oils has been successfully innovated and implemented. Various extraction methods including solid-phase extraction, liquid-liquid extraction using methanol, acetonitrile, and water were studied. Water was by far the most efficient solvent for use as an extraction medium. Separation of the analytes was conducted using a 4.6 mm × 250 mm × 3.5 μm Agilent Zorbax column while detection and quantitation were conducted with a variable wavelength UV detector. Detection limits of all furans were at 1 ppb v/v with linear ranges range from 5 to 1000 ppb v/v with correlation coefficients of 0.997 or better. A relative standard deviation of at most 2.4% at 1000 ppb v/v and 7.3% at 5 ppb v/v and a recovery from 43% to 90% depending on the analyte monitored were obtained. The method was purposely designed to be environmental friendly with water as an extraction medium. Also, the method uses 80% water and 20% acetonitrile with a mere 0.2 mL/min of acetonitrile in an acetonitrile/water mixture as mobile phase. The analytical technique has been demonstrated to be highly reliable with low cost of ownership, suitable for deployment in quality control labs or in regions where available analytical resources and solvents are difficult to procure.