Polymerized impurities in β-lactam antibiotics can induce allergic reactions, which seriously threaten the health of patients. In order to study the polymerized impurities in cefoxitin sodium for injection, a novel approach based on the use of two-dimensional liquid chromatography coupled with time-of-flight mass spectrometry (2D-LC-TOF MS) was applied. In the 1st dimension, high performance size exclusion chromatography (HPSEC) with a TSK-G2000SWxl column was employed. Column switching was applied for the desalination of the mobile phase used to separate polymerized impurities in the 1st dimension before they were transferred to the 2nd dimension which utilized reversed phase liquid chromatography (RP-LC) and TOF MS for further structural characterization. The structures of four polymerized impurities (which were all previously unknown) in cefoxitin sodium for injection were deduced based on the MS² data. One novel polymerized impurity (PI-I), with 2H less than the molecular weight of two molecules of cefoxitin (Mr. 852.09), was found to be the most abundant (>50 %) in almost all the samples examined and could be regarded as the marker polymer of cefoxitin sodium for injection. This work also showed the great potential of the 2D-LC-TOF MS approach in structural characterization of unknown impurities separated with a mobile phase containing non-volatile phosphate in the 1st dimension.

A rapid classification method was developed for the malignant and benign thyroid nodules with ultrasound guided-fine needle aspiration biopsy (FNAB) samples. With probe electrospray ionization mass spectrometry, the mass-scan data of FNAB samples were used as datasets for machine learning. The patients were marked as malignant (98 patients), benign (110 patients) or undetermined (42 patients) by experienced doctors in terms of ultrasound, the B-Raf (BRAF) gene, and cytopathology inspections. Pairwise coupling was performed on 163 ions to generate 3630 ion ratios as new features for classifier training. With the new features, the performance of the multilayer perception (MLP) classifier is much better than that with the 163 ions as features directly. After training, the accuracy of the MLP classifier is as high as 92.0%. The accuracy of the single-blind test is 82.4%, which proved the good generalization ability of the MLP classifier. The overall concordance is 73.0% between prediction and six-month follow-up for patients in the undetermined group. Especially, the classifier showed high accuracy for the undetermined patients with suspicious for papillary carcinoma diagnosis (90.9%). In summary, the machine learning method based on FNAB samples has potential for real clinical applications.

In recent years, global consumption of seaweed has been on the rise, yet there is a lack of accurate methods for assessing the health risks associated with its consumption. This gap could pose a threat to public safety or lead to wasted resources by government regulatory bodies. In this study, animal models and an innovative PBET (IPBET) were used to determine the bioavailability and bioaccessibility of arsenic (As) and cadmium (Cd) in various seaweeds, followed by an in vivo-in vitro correlation (IVIVC) analysis. The results indicated that the bioaccessibility of As and Cd was strongly correlated with As-RBA and Cd-RBA, highlighting the method's significant potential for predicting As-RBA and Cd-RBA in seaweeds. Additionally, the study explored the impact of mineral elements on the bioaccessibility of As and Cd in seaweed, finding a significant influence. In vivo metabolism experiments in mice revealed that different As species undergo several biochemical transformations in the body, including oxidation, reduction, methylation, and demethylation, all of which should not be overlooked. Building on previous studies, we have developed a new perspective for risk assessment of combined As and Cd exposure in seaweed. Calculations of the target hazard quotient (THQ) and total risk (TR) indicated that factoring in the bioavailability of heavy metals, As species, and biotransformation rates (BR) substantially reduces the health risks associated with seaweed consumption. However, for populations consuming seaweed over the long term, there still remains a certain level of risk to human health.

Background: Periodontitis is a growing public health concern worldwide. Salivary volatile metabolites have emerged as promising biomarkers for the diagnosis of periodontal disease. However, research on the collection and identification of these metabolites in periodontitis patients remains limited. Objectives: To explore methods for collecting and identifying salivary volatile metabolites in periodontitis patients and investigate their potential as biomarkers for diagnosing periodontal disease. Method: Oral exhaled breath and saliva samples were collected from 115 periodontitis patients and 35 healthy individuals, divided into four cohorts. The discovery cohort (Periodontitis: P = 55, Healthy: H = 23) and the test cohort ( P = 48, H = 23) were screened and validated for potential biomarkers in volatile metabolites from oral exhaled breath by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The validation cohort 1 ( P = 12, H = 12) was tested for volatile metabolites in saliva by solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS), while validation cohort 2 ( P = 55, H = 23) was tested for metabolic pathways in saliva by liquid chromatography-mass spectrometry (LC-MS). Result: A total of 78 Volatile organic compounds (VOCs) were detected by TD-GC-MS, with 14 differential VOCs identified. A diagnostic model was established using cyclohexanone, styrene, and ethanol, yielding a combined AUC of 0.8237. These metabolites were also detected in saliva by SPME-GC-MS, with cyclohexanone showing higher expression in the periodontitis group ( P < 0.05). The caprolactam degradation pathway was a key source of volatile metabolites in the oral exhaled breath of periodontitis patients. Conclusion: We developed a novel method for analyzing salivary volatile metabolites using TD-GC-MS, demonstrating potential for periodontitis diagnosis. Cyclohexanone is identified as a potential biomarker for periodontitis, and the caprolactam degradation pathway may play a significant role in future studies on oral microbiota dysbiosis in periodontitis patients.

Construct a prediction model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) using Probe Electrospray Ionization Mass Spectrometry (PESI - MS) combined with artificial intelligence (AI), to assist in the preoperative prediction of lymph node metastasis in thyroid carcinoma by intraoperative frozen pathology. A total of 78 fresh tissue samples of PTC and their adjacent normal tissues were collected. After proper processing, these samples were subjected to detection and analysis using PESI - MS. Subsequently, a classification prediction model was established based on the mass spectrometry test results integrated with AI algorithms. Support vector machine (SVM), random forest (RF), multi - layer perceptron (MLP), and Gradient boosting classifier (GBC) were employed for model building. Employing Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) to conduct a single-blinded test on 10 independent PTC samples with unknown lymph node metastasis status. The SVM, and MLP algorithms achieved an accuracy of 100 % in differentiating PTC with or without LNM, while the RF and GBC algorithm reached an accuracy of 92 %. All four algorithms demonstrated an accuracy of 100 % in distinguishing PTC from adjacent normal tissues. The combination of PESI - MS and AI exhibits high accuracy in predicting LNM in PTC and performs remarkably well in the rapid diagnosis of PTC. This approach can effectively assist in the rapid diagnosis of intraoperative pathology, assist in determining the surgical scope of thyroid lymph node dissection, and offer more precise treatment for patients.