Objective To explore a novel method for early lung cancer screening based on exhaled breath analysis. MethodsThis study enrolled patients with suspected pulmonary malignancies and healthy individuals undergoing physical examinations at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Qingchun and Qiantang campuses) from September 2023 to June 2024. Enrolled subjects were categorized into a lung cancer group, a benign nodule/tumor group, and a healthy control group. Exhaled breath samples were collected using a sensor array constructed from multiple graphene composite materials to capture breath fingerprints. Based on the collected data, screening and diagnostic models for lung cancer were developed and their performance was evaluated. ResultsA total of 4 580 subjects were included. Among them, 3 195 were pathologically diagnosed with pulmonary malignancies, including 1 394 males and 1 801 females with a mean age of (58.93±12.37) years, 599 were diagnosed with benign nodules/tumors including 339 males and 260 females with a mean age of (57.10±11.06) years, and 786 were healthy controls with no pulmonary nodules detected on chest CT including 420 males and 366 females with a mean age of (29.75±9.32) years. The screening model for high-risk populations (distinguishing patients with lung cancer/high-risk pulmonary nodules from healthy individuals) demonstrated excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.926. At the optimal Youden’s index (cutoff threshold of 63.5%), the external test set achieved a specificity of 85.2%, a sensitivity of 88.4%, and an accuracy of 86.8%. The diagnostic model (distinguishing patients with lung cancer/premalignant lesions from those with benign pulmonary nodules/healthy individuals) achieved an AUC of 0.818. At its optimal Youden’s index (cutoff threshold of 47.0%), the external test set showed a specificity of 71.7%, a sensitivity of 77.3%, and an accuracy of 74.5%. ConclusionThe non-invasive breath analysis platform based on a sensor array, developed in this study, can achieve rapid and relatively accurate lung cancer screening by analyzing breath fingerprints. This confirms the feasibility of this technology for early lung cancer screening and holds promise for facilitating the early detection and intervention of lung cancer.
ObjectiveTo explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). MethodsA systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). ResultsA total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. ConclusionThe exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.
ObjectiveTo observe the clinical effect of intravitreal injection of tissue plasminogen activator (t-PA), ranibizumab and C3F8 in the treatment of early submacular hemorrhage (SMH) induce to polypoid choroidal vasculopathy (PCV).MethodsThe clinical data of 20 eyes of 20 patients with early SMH induce to PCV were enrolled in this study. The duration of bleeding in the eye was 7 to 28 days, and the mean duration of bleeding was 14.8±5.6 days. All eyes are measured using the Snellen chart best corrected visual acuity (BCVA), logarithm of the minimum angle of resolution (logMAR) was used to calculate visual acuity. Measure central retinal thickness (CRT) and central retinal pigment epithelial detachment (PED) thickness using frequency-domain optical coherence tomography. The average logMAR BCVA of eyes was 1.73±0.91; the mean CRT was 620.0±275.8 μm; the average central PED thickness was 720.3±261.9 μm. All eyes receive intravitreal injection of t-PA, ranibizumab and C3F8. The intravitreal injection of ranibizumab was administered once a month for 3 consecutive months, followed by an on-demand treatment plan. Mean follow-up time was 9.9±3.6 months. The changes in BCVA, CRT, central PED thickness and clearance degree of SMH at 6 months after treatment were observed.ResultsOn the 6 months after treatment, the average logMAR BCVA, CRT and central PED thickness of the eyes were respectively 0.42±0.37, 290.2±97.4 μm and 41.6±78.1 μm. Compared with baseline, the after treatment BCVA was significantly increased (F=38.14, P=0.000), but the CRT and central PED were significantly decreased (F=7.48, 75.94; P=0.000, 0.000). Among the 20 eyes, 16 eyes of SMH was completely cleared, accounting for 80%;4 eyes was partially cleared, accounting for 20%. No recurrence and systemic or local complications occurred during follow-up of all eyes.ConclusionIntravitreal injection of t-PA, ranibizumab, and C3F8 in the treatment of early SMH induce to PCV can effectively remove SMH, improve vision, reduce CRT and central thickness of PED.
At present, tamponade agent which being used in retinal surgery is mainly sterile air, gas and silicone oil. Sterile air is mostly used in the treatment of simple retinal detachment. Gas or silicone oil as tamponade is greatly applied for complicated retinal detachment. In recent years, with the application of micro-invasive vitrectomy under a wide-angle viewing system and perioperative anti-vascular endothelial growth factor drugs, application of intraocular filling materials also has changed. The application of silicone oil is significantly reduced. Percentage rate of gas as tamponade for retinal detachment is reduced. The application of sterile air as tamponade is rising. With selecting indication carefully and picking up the suitable air or gas, doctor will reduce the workload. It will also reduce the social burden and benefit patients.
The microarray technology used in biological and medical research provides a new idea for the diagnosis and treatment of cancer. To find different types of cancer and to classify the cancer samples accurately, we propose a new cluster ensemble framework Dual Neural Gas Cluster Ensemble (DNGCE), which is based on neural gas algorithm, to discover the underlying structure of noisy cancer gene expression profiles. This framework DNGCE applies the neural gas algorithm to perform clustering not only on the sample dimension, but also on the attribute dimension. It also adopts the normalized cut algorithm to partition off the consensus matrix constructed from multiple clustering solutions. We obtained the final accurate results. Experiments on cancer gene expression profiles illustrated that the proposed approach could achieve good performance, as it outperforms the single clustering algorithms and most of the existing approaches in the process of clustering gene expression profiles.
In the clinical practice, the mechanical ventilation is a very important assisting method to improve the patients' breath. Whether or not the parameters set for the ventilator are correct would affect the pulmonary gas exchange. In this study, we try to build an advisory system based on the gas exchange model for mechanical ventilation using fuzzy logic. The gas exchange mathematic model can simulate the individual patient's pulmonary gas exchange, and can help doctors to learn the patient's exact situation. With the fuzzy logic algorithm, the system can generate ventilator settings respond to individual patient, and provide advice to the doctors. It was evaluated in 10 intensive care patient cases, with mathematic models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Compared to the ventilator set only as part of routine clinical care, the present system could reduce the inspired oxygen fraction, reduce the respiratory work, and improve gas exchange with the model simulated outcome.