简介：AbstractOver the last 20 years, it has become possible to use a precision medicine approach to the management of chronic obstructive pulmonary disease (COPD). Clinical and physiological features as well as a blood biomarker can be used to target treatments to patients most likely to benefit and avoid treatment in patients less likely to benefit. Future advances in a precision medicine approach to COPD will depend on more precise characterization of individual patients, possibly using quantitative imaging, new physiological techniques, novel biomarkers and genetic profiling. Precision medicine has led to significant improvements in the management of COPD and clinicians should use all available information to optimize the treatment of individual patients.

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简介：AbstractBackground:Fibrosis in the peripheral airways contributes to airflow limitation in patients with chronic obstructive pulmonary disease (COPD). However, the key proteins involved in its development are still poorly understood. Thus, we aimed to identify the differentially expressed proteins (DEPs) between smoker patients with and without COPD and elucidate the molecular mechanisms involved by investigating the effects of the identified biomarker candidate on lung fibroblasts.Methods:The potential DEPs were identified by isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis. The messenger RNA and protein levels of clusterin (CLU) in COPD patients and 12% cigarette smoke extract (CSE)-treated human bronchial epithelial cells were determined at the indicated time points. Furthermore, an in vitro COPD model was established via the administration of 8% CSE to normal human lung fibroblasts (NHLFs) at indicated time points. The effects of CSE treatment and CLU silencing on proliferation and activation of lung fibroblasts were analyzed.Results:A total of 144 DEPs were identified between COPD patients and normal smokers. The iTRAQ-based proteomics and bioinformatics analyses identified CLU as a serum biomarker candidate. We also discovered that CLU levels were significantly increased (P < 0.0001) in Global Initiative for Obstructive Lung Disease II, III, and IV patients and correlated (P < 0.0001) with forced expiratory volume in 1 s (R=-0.7705), residual volume (RV) (R = 0.6281), RV/total lung capacity (R = 0.5454), and computerized tomography emphysema (R = 0.7878). Similarly, CLU levels were significantly increased in CSE-treated cells at indicated time points (P < 0.0001). The CSE treatment significantly inhibited the proliferation, promoted the inflammatory response, differentiation of NHLFs, and collagen matrix deposition, and induced the apoptosis of NHLFs; however, these effects were partially reversed by CLU silencing.Conclusion:Our findings suggest that CLU may play significant roles during airway fibrosis in COPD by regulating lung fibroblast activation.

简介：AbstractBackground:Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM.Methods:Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography–mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM.Results:The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75–0.91) in the training set and 0.79 (0.65–0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74–0.93) and 0.82 (0.66–0.98), respectively.Conclusions:The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.

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