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.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
The use of non-invasive blood glucose detection techniques can help diabetic patients to alleviate the pain of intrusive detection, reduce the cost of detection, and achieve real-time monitoring and effective control of blood glucose. Given the existing limitations of the minimally invasive or invasive blood glucose detection methods, such as low detection accuracy, high cost and complex operation, and the laser source's wavelength and cost, this paper, based on the non-invasive blood glucose detector developed by the research group, designs a non-invasive blood glucose detection method. It is founded on dual-wavelength near-infrared light diffuse reflection by using the 1 550 nm near-infrared light as measuring light to collect blood glucose information and the 1 310 nm near-infrared light as reference light to remove the effects of water molecules in the blood. Fourteen volunteers were recruited for in vivo experiments using the instrument to verify the effectiveness of the method. The results indicated that 90.27% of the measured values of non-invasive blood glucose were distributed in the region A of Clarke error grid and 9.73% in the region B of Clarke error grid, all meeting clinical requirements. It is also confirmed that the proposed non-invasive blood glucose detection method realizes relatively ideal measurement accuracy and stability.
ObjectiveTo evaluate the clinical value of a combined diagnostic model integrating circulating cell-free DNA (cfDNA) methylation markers and CT imaging features for differentiating benign and malignant lung nodules and for early lung cancer detection. This study pioneers a two-step multi-omics modeling approach to construct a robust diagnostic model. MethodsA retrospective cohort of 140 patients (70 malignant and 70 benign, confirmed by postoperative pathology) with lung nodules who underwent surgical treatment at West China Hospital, Sichuan University, from January 2014 to December 2024 was included. Methylation profiles of 54 cfDNA regions were detected via targeted methylation sequencing. CT imaging features (e.g., nodule size, type, and signs) were extracted. A two-step modeling strategy was applied: ① imaging features were modeled directly using binary logistic regression, while methylation features were selected via LASSO regression before modeling; ② a combined model was constructed using the scores from both models. Model performance was evaluated using receiver operating characteristic (ROC) curves, with internal validation via Bootstrap (1000 iterations). ResultsAll patients were split into a training set (n=84) and a test set (n=56). In the test set, the combined model achieved an area under the ROC curve (AUC) of 0.86 [95% confidence interval (CI): 0.74-0.95], with both sensitivity and specificity reaching 82%. This outperformed the individual imaging model (AUC=0.74) and methylation model (AUC=0.82). ConclusionThe multi-omics combined diagnostic model significantly improved the ability to distinguish benign from malignant lung nodules, particularly for early-stage lesions like ground-glass opacities. Its non-invasive and high-sensitivity features provide a promising translational tool for lung cancer screening, with promising clinical application prospects.
The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.
At present, the monitoring methods fwor intracranial pressure adopted in clinical practice are almost all invasive. The invasive monitoring methods for intracranial pressure were accurate, but they were harmful to the patient's body. Therefore, non-invasive methods for intracranial pressure monitoring must be developed. Since 1980, many non-invasive methods have been sprung out in succession, but they can not be used clinically. In this paper, research contents and progress of present non-invasive intracranial pressure monitoring are summarized. Advantages and disadvantages of various ways are analyzed. And finally, perspectives of development for intracranial pressure monitoring are presented.
ObjectiveTo observe the predictive value of Volume OXygeneration (VOX) index for early non-invasive positive pressure ventilation (NIPPV) treatment in patients with type I Respiratory failure. MethodsRetrospective analysis was made on the patients with type I Respiratory failure admitted to the intensive care medicine from September 2019 to September 2022, who received early NIPPV treatment. After screening according to the discharge standard, they were grouped according to the NIPPV 2-hour VOX index. The observation group was VOX Youden index >20.95 (n=69), and the control group was VOX index ≤20.95 (n=64). Collect patient baseline data and NIPPV 2-hour, 12-hour, and 24-hour arterial blood gas values, and calculate NIPPV outcomes, intubation status, NIPPV usage time, hospital stay, and mortality rate. ResultsThere was a statistically significant difference in respiratory rate (RR) between the baseline data onto the two groups of patients, but others not. After early NIPPV treatment, the 2-hour oxygenation index (P/F) [(182.5 ± 66.14) vs. (144.1 ± 63.6) mm Hg, P<0.05] of the observation group showed a more significant increase. The failure rate of NIPPV intubation within 12 hours was lower (4.35% vs. 32.81%, P<0.05), the success rate of NIPPV withdrawal from 24 hours was higher (40.58% vs. 0%, P<0.05), and the failure rate of NIPPV intubation was lower (4.35% vs. 46.88%, P<0.05). The comparison of treatment outcomes showed that the intubation rates in the observation group (4.35% vs. 67.19%, P<0.05) was lower. The threshold of NIPPV 2-hour VOX index 20.95 was used as a predictor of Tracheal intubation, with sensitivity of 74.7% and specificity of 93.5%. ConclusionIn the early NIPPV treatment of patients with type I Respiratory failure, the NIPPV 2-hour VOX index>20.91 is taken as the evaluation index, which can better to predict the improvement in hypoxia and the risk of NIPPV failure Tracheal intubation, and has clinical significance.
ObjectiveTo systematically evaluate the efficacy of high-flow nasal cannula oxygen therapy (HFNC) in Post-extubation acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients. MethodsThe Domestic and foreign databases were searched for all published available randomized controlled trials (RCTs) about HFNC therapy in post-extubation AECOPD patients. The experimental group was treated with HFNC, while the control group was treated with non-invasive positive pressure ventilation (NIPPV). The main outcome measurements included reintubation rate. The secondary outcomes measurements included oxygenation index after extubation, length of intensive care unit (ICU) stay, mortality, comfort score and adverse reaction rate. Meta-analysis was performed by Revman 5.3 software. ResultA total of 20 articles were enrolled. There were 1516 patients enrolled, with 754 patients in HFNC group, and 762 patients in control group. The results of Meta-analysis showed that there were no significant difference in reintubation rate [RR=1.41, 95%CI 0.97 - 2.07, P=0.08] and mortality [RR=0.91, 95%CI 0.58 - 1.44, P=0.69]. Compared with NIPPV, HFNC have advantages in 24 h oxygenation index after extubation [MD=4.66, 95%CI 0.26 - 9.05, P=0.04], length of ICU stay [High risk group: SMD –0.52, 95%CI –0.74 - –0.30; Medium and low risk group: MD –1.12, 95%CI –1.56- –0.67; P<0.00001], comfort score [MD=1.90, 95%CI 1.61 - 2.19, P<0.00001] and adverse reaction rate [RR=0.22, 95%CI 0.16 - 0.31, P<0.00001]. ConclusionsCompared with NIPPV, HFNC could improve oxygenation index after extubation, shorten the length of ICU stay, effectively improve Patient comfort, reduce the occurrence of adverse reactions and it did not increase the risk of reintubation and mortality. It is suggested that HFNC can be cautiously tried for sequential treatment of AECOPD patients after extubation, especially those who cannot tolerate NIPPV.
Non-invasive biomarkers, due to their non-invasive and safe characteristics, hold significant potential for the diagnosis and prognosis of epilepsy. This review summarizes the research progress and future directions of non-invasive biomarkers for epilepsy, encompassing electrophysiological, imaging, biochemical, and genetic markers, and discusses biomarkers for specific types of epilepsy, such as structural lesion-related epilepsy, infection and inflammation-related epilepsy, autoimmune epilepsy, endocrine hormone-related epilepsy, and metabolic epilepsy, to facilitate their clinical application.
The prevalence of cardiovascular disease in our country is increasing, and it has been a big problem affecting the social and economic development. It has been demonstrated that early intervention of cardiovascular risk factors can effectively reduce cardiovascular disease-caused mortality. Therefore, extensive implementation of cardiovascular testing and risk factor screening in the general population is the key to the prevention and treatment of cardiovascular disease. However, the categories of devices available for quick cardiovascular testing are limited, and in particular, many existing devices suffer from various technical problems, such as complex operation, unclear working principle, or large inter-individual variability in measurement accuracy, which lead to an overall low popularity and reliability of cardiovascular testing. In this study, we introduce the non-invasive measurement mechanisms and relevant technical progresses for several typical cardiovascular indices (e.g., peripheral/central arterial blood pressure, and arterial stiffness), with emphasis on describing the applications of biomechanical modeling and simulation in mechanism verification, analysis of influential factors, and technical improvement/innovation.