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Objective: To investigate the clinical use of Macao predictive values of impulse oscillometry(IOS) for chronic obstructive pulmonary disease (COPD) in patients aged over 45 years.
Methods: We measured lung impedance with IOS and spirometry in healthy subjects (n=168) and patients with COPD (n=281) aging over 45 years. The spirometric parameters were compared with those of IOS calculated by Macao predictive equations with Lechtenboerger equations.
Results: Respiratory impedance (Zrs), respiratory resistance at 5 Hz (R5), R5-R20 in female COPD group were (0.72±0.28), (0.63±0.23)and(0.23±0.16) kPa·L(-1)·s(-1), respectively, and Fres was (22±7)Hz; while in male group, the value of each parameters was (0.56±0.21), (0.50±0.17) and(0.18±0.12) kPa·L(-1)·s(-1), Fres was(21±7)Hz, which were all greater than that of the healthy group(t value was 5.05, 4.30, 5.10, 6.05 and 8.27, 6.62, 12.68, 14.59, respectively; P value were all<0.01). Reactance at 5 Hz(X5) in the COPD group[(-0.30±0.21) kPa·L(-1)·s(-1) in female, (-0.26±0.16) kPa·L(-1)·s(-1) in male] was significantly lower than that in the healthy group[female group: X5=(-0.16±0.06) kPa·L(-1)·s(-1,) t value was -5.38; male group: X5=(-0.10±0.05) kPa·L(-1)·s(-1,) t value was -11.96, P value were all<0.01]. Zrs, R5, R5-R20 and Fres were negatively correlated with parameters (FEV1/FVC, FEV1%Pre) of spirometry, while X5 was positively correlated with them. Compared with the ROC areas under the curve(AUC), the AUC of Zrs(A/P2) (0.786 in female, 0.773 in male) was same as that of Zrs(A)(0.744 in female, 0.764 in male; χ(2) value was 4.96, 0.89, respectively, P value were all >0.05), the AUC of R5(A/P2)(0.754 in female, 0.741 in male) was larger than that of R5(A/P1) (both were 0.716; χ(2) value was 4.24, 6.38, respectively, P value were all <0.05). The AUC of X5(P2-A) was larger than that of X5(P1-A) in the male group, and it was same as in the female group. The first two AUC of IOS parameters were Fres and R5-R20. In the 2 groups, when Zrs (A/P2)% was larger than 130, R5(A/P2)% was larger than 130, X5(P2-A)was larger than 0.1, Fres was larger than 15 in male, 20 in female, their each Youden's index was 0.463, 0.398, 0.662 and 0.594, each accuracy was 84%, 71%, 81% and 82%, which were all greater than that of Lechtenboerger equations(66%, 63%, 80% and 50%).
Conclusion: There are good correlations between spirometry and respiratory impendance measured by IOS in the diagnosis of COPD. The Macao predictive equations have higher sensitivity and specificity for diagnosing COPD.
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http://dx.doi.org/10.3760/cma.j.issn.1001-0939.2016.01.012 | DOI Listing |
Med Phys
September 2025
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.
Background: Dual-energy computed tomography (DECT) enhances material differentiation by leveraging energy-dependent attenuation properties particularly for carbon ion therapy. Accurate estimation of tissue elemental composition via DECT can improve quantification of physical and biological doses.
Objective: This study proposed a novel machine-learning-based DECT (ML-DECT) method to predict the physical density and mass ratios of H, C, N, O, P, and Ca.
Brain Behav
September 2025
Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China.
Background: Delirium is an acute cognitive disturbance that is linked to increased healthcare costs, extended hospitalization, and a greater incidence of adverse outcomes, including cognitive decline. Despite its clinical importance, existing strategies for predicting and managing delirium remain inadequate. This study, therefore, sought to investigate the potential relationship between cerebrospinal fluid proteins and delirium via Mendelian randomization (MR) and to identify potential therapeutic targets.
View Article and Find Full Text PDFACS Omega
September 2025
Centre of Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, Macao SAR 999078, China.
Tyrosinase, a copper-dependent oxidase, plays a critical role in melanin biosynthesis and is a target in skin-whitening cosmetics. Conventional inhibitors like arbutin and kojic acid are widely used but suffer from cytotoxicity, instability, and inconsistent efficacy, highlighting the need for safer, more effective alternatives. In this study, two ligand-based machine learning models were developed: one to predict the biological activity of compounds and the other to estimate specific pIC values.
View Article and Find Full Text PDFBiosaf Health
August 2025
Faculty of Innovation Engineering, Macau University of Science and Technology, 999078, Macao Special Administrative Region of China.
Understanding human-virus protein-protein interactions is critical for studying molecular mechanisms driving viral infection, immune evasion, and propagation, thereby informing strategies for public health. Here, we introduce a novel multimodal deep learning framework that integrates high-confidence experimental datasets to systematically predict putative interactions between human and viral proteins. Our approach incorporates two complementary tasks: binary classification for interaction prediction and conditional sequence generation to identify interacting protein partners.
View Article and Find Full Text PDFComput Biol Chem
September 2025
Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao Special Administrative Region of China. Electronic address:
With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developing predictive models of drug responses and identifying associated biomarkers. However, many currently available predictive models are often just used as black boxes, lacking meaningful biological interpretations. In this study, we made use of open-source drug response data from cancer cell lines, in conjunction with KEGG pathway information, to develop sparse neural networks, K-net, enabling the prediction of drug response in EGFR signaling pathways and the identification of key biomarkers.
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