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Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.

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There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

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Correction for 'Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis' by Qiuyao Du et al., Analyst, 2023, https://doi.org/10.1039/d3an01051a.

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Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

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Simultaneous imaging of exogenous nanomaterials and endogenous metabolites in situ remains challenging and is beneficial for a systemic understanding of the biological behavior of nanomaterials at the molecular level. Here, combined with label-free mass spectrometry imaging, visualization and quantification of the aggregation-induced emission nanoparticles (NPs) in tissue were realized as well as related endogenous spatial metabolic changes simultaneously. Our approach enables us to identify the heterogeneous deposition and clearance behavior of nanoparticles in organs. The accumulation of nanoparticles in normal tissues results in distinct endogenous metabolic changes such as oxidative stress as indicated by glutathione depletion. The low passive delivery efficiency of nanoparticles to tumor foci suggested that the enrichment of NPs in tumors did not benefit from the abundant tumor vessels. Moreover, spatial-selective metabolic changes upon NPs mediated photodynamic therapy was identified, which enables understanding of the NPs induced apoptosis in the process of cancer therapy. This strategy allows us to simultaneously detect exogenous nanomaterials and endogenous metabolites in situ, hence to decipher spatial selective metabolic changes in drug delivery and cancer therapy processes.

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Fingerprints contain important information such as the ingredients ingested by the donor. By analyzing the characteristic components in fingerprints, the donor can be characterized, which would provide insights for investigation of a given case. This approach can also be used in the qualitative monitoring of drug intake. Therefore, the examination of hypotensive drugs in fingerprints has significant value in practical application. This study established a method based on ultra performance liquid chromatography-triple quadrupole composite linear ion trap mass spectrometry (UPLC-Q-TRAP/MS) for the simultaneous determination of 36 hypotensive drugs in fingerprints. The pre-treatment method was based on protein precipitation. A 3×3 cm filter paper was cut into pieces and placed in a 2 mL plastic centrifuge tube after fingerprint collection. Then, 0.50 mL methanol was added, followed by vortex mixing for 1 min and ultrasonic oscillation for 3 min. The filter paper was centrifuged at 12000 r/min for 5 min, and the supernatant was withdrawn for sample analysis. An ACQUITY UPLC® BEH C18 chromatographic column (100 mm×3.0 mm, 1.7 µm) was selected, with 0.01% aqueous formic acid and methanol as mobile phases for gradient elution. MS analysis involved scheduled multiple reaction monitoring-information dependent acquisition-enhanced product ion (SMRM-IDA-EPI) scanning. This method could be used to retrieve library researching during high-sensitivity analysis, which could increase the accuracy of qualitative results. The calibration curves showed good linearity in the range of 0.05-50.00 ng/fingerprint, with correlation coefficients (r) greater than 0.99 for all 36 analytes. The limits of detection and limits of quantification of the 36 hypotensive drugs were 0.001-0.045 ng/fingerprint and 0.002-0.050 ng/fingerprint, respectively. At spiked levels of 0.25, 2.50, 25.00 ng/fingerprint, the matrix effects, recoveries, intra-day precisions, and inter-day precisions of the 36 hypotensive drugs were 79.0%-119.2%, 79.3%-116.2%, 0.2%-18.3%, and 1.6%-19.1%, respectively. This method was used to detect hypotensive drugs in the fingerprints of 87 hypertensive patients, and hypotensive drug intakes were accurately detected in most cases. The established method is operationally simple, with high sensitivity and good selectivity, and it is suitable for screening and testing hypotensive drugs in fingerprints.

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RATIONALE: The objective of this study was to develop, optimize, and validate a method for the determination and quantification of 17 hypoglycemic drugs in fingerprints using ultra-high-performance liquid chromatography/tandem hybrid triple quadrupole linear ion trap mass spectrometry (UHPLC/QTRAP-MS/MS). We also aimed to apply the present method to the fingerprints collected from patients with hyperglycemia. METHODS: The scheduled multiple reaction monitoring information-dependent acquisition-enhanced product ion (SMRM-IDA-EPI) scanning mode was utilized. The chromatographic system consisted of an Acquity UHPLC® BEH C18 column (3.0 × 100 mm, 1.7 µm) and a mobile phase of 0.01% (v/v) formic acid in water and methanol. Analytes were extracted via a precipitation protein procedure. The method was validated in accordance with the US Food and Drug Administration (FDA) guidance and applied to the analysis of fingerprint deposits from subjects who had taken the drugs. RESULTS: The limits of detection (LODs) and the lower limits of quantification (LLOQs) of 17 hypoglycemic drugs were 0.001 to 0.020 and 0.002 to 0.050 ng/fingerprint, respectively. The correlation coefficients (r) for the calibration curves were > 0.99 in the range of 0.050-50.000 ng/fingerprint. The matrix effect and recovery of 17 hypoglycemic drugs at three concentrations ranged from 81.1 to 117.3% and 80.0 to 109.6%, respectively. The validation data (intra- and inter-day combined) for accuracy ranged from 85.5 to 117.2%, the CV (%) data were ≤19.7%. All analytes were found to be stable stored in the autosampler (4°C) for 24 h. This validated method was successfully applied to detect hypoglycemic drugs in fingerprints from patients with hyperglycemia. CONCLUSIONS: A quantification method for hypoglycemic drugs in fingerprints was developed, optimized, and validated. This sensitive method could be used for drug monitoring and providing reference information in forensic investigations.

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An ultra-performance liquid chromatography tandem triple quadrupole compound linear ion trap mass spectrometry (UPLC-Q-TRAP/MS) method was developed and validated for the detection of hypolipidemic drugs in fingerprints. 13 hypolipidemic drugs were well separated by the gradient elution of 0.01% formic acid in water and methanol at a flow rate of 0.4 mL/min within 11 min. The analytes were detected in positive (ESI+) and negative (ESI-) modes and scanned using scheduled multiple reaction monitoring-information dependent acquisition-enhanced product ion (SMRM-IDA-EPI) for best selectivity and sensitivity. The calibration curves showed good linearity in the range of 0.050-50.000 ng/patch with coefficients (r2) higher than 0.9904 for all analytes. Meantime, the LODs and LLOQs were in ranges of 0.001-0.034 and 0.003-0.050 ng/patch. The accuracies, intra-day and inter-day precision ranged from -13.3 to 0.3%, 1.1-10.4% and 3.7-14.5%, respectively. The recoveries ranged from 79.9 to 114.8%, while the absolute and relative matrix effects were in the range of 83.0-107.2% and 2.2-9.7%. By comparing the non-spiked fingerprints from healthy volunteers with the fingerprints obtained from patients, demonstrated that the method was competent for determination and quantitation of hypolipidemic drugs in fingerprints.

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