Although dipeptidyl peptidase-4 (DPP-4) inhibitors have been implicated in the development of acute pancreatitis, the causality of this phenomenon is not well established. We herein report the case of an 85-yearold woman who presented with epigastric pain after taking saxagliptin for five months. A high serum lipase level with characteristic computed tomography findings confirmed the diagnosis of acute pancreatitis. The patient's symptoms rapidly resolved after admission, although they recurred when she resumed treatment with saxagliptin for 18 days after discharge. In the absence of any identifiable causes of pancreatitis and considering the temporal sequence of events, the saxagliptin therapy was highly suspected to be the triggering factor. Although drug-induced pancreatitis is rare, treatment with DPP-4 inhibitors should be included as a potential etiology of acute pancreatitis.

BACKGROUND Electronic nose (eNose) can differentiate between healthy individuals and patients with specific respiratory diseases. However, its application in discriminating among various respiratory diseases is limited. OBJECTIVE We aimed to investigate the feasibility of using a novel eNose to differentiate between patients with three respiratory disease entities, including lung cancer, pneumonia, and structural lung disease, and healthy control group. Additionally, we validated the results using gas chromatography mass spectrometry validation (GC-MS) quantitative analysis. METHODS Patients with lung cancer, pneumonia, and structural lung diseases, along with healthy participants were recruited between May 2019 to July 2022. Exhaled breath samples were collected for eNose and GC-MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed. RESULTS A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC:1-0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC:1), followed by lung cancer and structural lung disease (AUC:0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients. CONCLUSIONS Our study demonstrated that the novel eNose can differentiate different respiratory diseases. The convenience, short turn-around time, and noninvasiveness of eNose make it a potential point-of-care test in clinical practice.

Introduction Human exhaled breath contains more than 1,000 of the vast variety of volatile organic compounds (VOCs), providing valuable information about the metabolic process in human beings. The information of breath could include current state of disease, leading to great potential of noninvasive diagnosis in medical industry 1-6. The concentration of the exhaled breath VOCs varies in sub-ppm or even lower in ppb level in healthy people7. However, when disease occurs in the human body, the metabolic process becomes disbalance. Hence, the concentration profile of exhaled breath VOCs drastically increases. Recently, deaths caused by lung cancer have reached 1.6 million each year8. Early screening and diagnosing of lung cancer are big challenges in the healthcare industry. There are many technologies to detect and diagnose lung cancer, such as low-dose chest computed tomography (CT), which enhances the likelihood of early-stage tumor diagnosis9, resulting in an increase in the survival rate10. However, the aforementioned technique still suffers from a large rate of false positive due to cross reactive responses10. In addition, due to the existence of a low dose of ionizing radiation in the CT, employing this technique increases the risk of cancer. Therefore, a noninvasive technology for breath analysis is desired to diagnose lung cancer. In this paper, we report a fast screening method of the lung cancer biomarker in exhaled breath by using gas chromatography-mass spectroscopy coupled with thermal desorption (TD-GC-MS), which is one of the noninvasive technologies that is able to perform this task. In the experiments, Tenax TA material is used as absorbent to absorb the exhaled breath VOCs, thermal desorption system is used to desorb the VOCs, which are separated by the gas chromatography column and further detected by the mass spectrometer. Method A 1 Liter Tedlar bag was used to sample the exhaled breath for the lung cancer patients at the NTU hospital Hsinchu. Further, the breath sample was transferred from Tedlar bag to Tenax TA tube with a flow rate of 40 cc/min for 25 minutes. An Agilent type7890A GC system with 5975C inert MSD with a triple-axis detector along with Perkin Elmer thermal desorption system (Turbo matrix 100) was used. Breath VOCs were separated by Elite-5 MS column (30 m × 0.25 mm, film thickness 0.25μm, Perkin Elmer) while working in a constant pressure mode (10 psi). The mass spectrometer was set to scan mode. The program of column temperature was maintained at 80 for 4 min, and then increased at a rate of 15 per min, to 230 and held at 230 for 4 min11. After breath sampling, Tenax TA absorbent was used to absorb the volatile organic compound, then the Tenax TA was connected to the Perkin Elmer thermal desorption system. Results and Conclusions Gas chromatography mass Spectro meter coupled with the thermal desorption system was used in the detection of lower concentration of the volatile organic compound in the exhaled breath for lung cancer patients. The thermal desorption process was used to desorb the volatile organic compound from the Tenax TA absorbent. The desorbed samples from Tenax TA absorbent were transferred through the column. The VOCs were moved into the column by the inert gas mobile phase; then, they were separated by the stationary phase fixed into the column. The separation efficiency depended upon the gas chromatography column. The detection principle of the GC-MS was based on the mass to charge ratio (M/Z) of the ionized atom for the detection of biomarkers in the lung cancer patients. After separation in gas chromatography, VOCs were detected by the mass spectrometer. The peak area of various kind of VOCs for lung cancer have been obtained. We have found some biomarkers from lung cancer’s exhaled breath such as acetone, toluene, ethyl benzene, decane, etc. In future, we will sample the breath for the control group, after analysis by GC-MS compare the result with lung cancer patient to identify the unique biomarkers. The Figure 1 shows the profile of the peak area with respect to the various kind of VOCs for the lung cancer patient. Therefore, the TD-GC-MS is an effective technique for noninvasive diagnosis by employing exhaled breath VOCs for the health care industry. These exhaled breath biomarkers can be used to screen the early lung cancerous disease to save millions of lives worldwide. Figure 1