Low Skeletal Muscle Mass and Clinical Outcomes in Chronic Obstructive Pulmonary Disease

Background In patients with chronic obstructive pulmonary disease (COPD), decreased muscle mass is a frequently encountered comorbidity in clinical practice. However, the evaluation of muscle mass in patients with COPD in real-world practice is rare. Methods We retrospectively reviewed the electronic medical records of all patients with COPD who underwent bioelectrical impedance analysis at least once between January 2011 and December 2021 in three hospitals. Then, we analyzed the performance rate of muscle mass measurement in the patients and the correlation between muscle mass, clinical parameters, and COPD prognosis. Results Among the 24,502 patients with COPD, only 270 (1.1%) underwent muscle mass measurements. The total skeletal muscle mass index was significantly correlated with albumin, alanine transaminase, and creatinine to cystatin C ratio in patients with COPD (r=0.1614, p=0.011; r=0.2112, p=0.001; and r=0.3671, p=0.001, respectively). Acute exacerbation of COPD (AE COPD) was significantly correlated with muscle mass, especially the truncal skeletal muscle mass index (TSMI) in males (r=–0.196, p=0.007). In the multivariate analysis, TSMI and cystatin C were significant risk factors for AE COPD (hazard ratio, 0.200 [95% confidence interval, CI, 0.048 to 0.838] and 4.990 [95% CI, 1.070 to 23.278], respectively). Conclusion Low muscle mass negatively affects the clinical outcomes in patients with COPD. Despite its clinical significance, muscle mass measurement is performed in a small proportion of patients with COPD. Therefore, protocols and guidelines for the screening of sarcopenia in patients with COPD should be established

Effects of breathing maneuver and sitting posture on muscle activity in inspiratory accessory muscles in patients with chronic obstructive pulmonary disease

BACKGROUND: To determine the influence of breathing maneuver and sitting posture on tidal volume (TV), respiratory rate (RR), and muscle activity of the inspiratory accessory muscles in patients with chronic obstructive pulmonary disease (COPD). METHODS: Twelve men with COPD participated in the study. Inductive respiratory plethysmography and surface electromyography were used to simultaneously measure TV, RR, and muscle activity of the inspiratory accessory muscles [the scalenus (SM), sternocleidomastoid (SCM), and pectoralis major (PM) muscles] during quiet natural breathing (QB) and pursed-lips breathing (PLB) in three sitting postures: neutral position (NP), with armm support (WAS), and with arm and head support (WAHS). RESULTS: Two-way repeated-measures analysis of variance was employed. In a comparison of breathing patterns, PLB significantly increased TV and decreased RR compared to QB. Muscle activity in the SM and SCM increased significantly in PLB compared to QB. In a comparison of sitting postures, the muscle activity of the SM, SCM, and PM increased in the forward-leaning position. CONCLUSIONS: The results suggest that in COPD, PLB induced a favorable breathing pattern (increased TV and reduced RR) compared to QB. Additionally, WAS and WAHS positions increased muscle activity of the inspiratory accessory muscles during inspiration versus NP. Differential involvement of accessory respiratory muscles can be readily studied in COPD patients, allowing monitoring of respiratory load during pulmonary rehabilitation

Roles of Inflammatory Biomarkers in Exhaled Breath Condensates in Respiratory Clinical Fields

Background Exhaled condensates contain inflammatory biomarkers; however, their roles in the clinical field have been under-investigated. Methods We prospectively enrolled subjects admitted to pulmonology clinics. We collected exhaled breath condensates (EBC) and analysed the levels of six and 12 biomarkers using conventional and multiplex enzyme-linked immunosorbent assay, respectively. Results Among the 123 subjects, healthy controls constituted the largest group (81 participants; 65.9%), followed by the preserved ratio impaired spirometry group (21 patients; 17.1%) and the chronic obstructive pulmonary disease (COPD) group (21 patients; 17.1%). In COPD patients, platelet derived growth factor-AA exhibited strong positive correlations with COPD assessment test (ρ=0.5926, p=0.0423) and COPD-specific version of St. George’s Respiratory Questionnaire (SGRQ-C) score (total, ρ=0.6725, p=0.0166; activity, ρ=0.7176, p=0.0086; and impacts, ρ=0.6151, p=0.0333). Granzyme B showed strong positive correlations with SGRQ-C score (symptoms, ρ=0.6078, p=0.0360; and impacts, ρ=0.6007, p=0.0389). Interleukin 6 exhibited a strong positive correlation with SGRQ-C score (activity, ρ=0.4671, p=0.0378). The absolute serum eosinophil and basophil counts showed positive correlations with pro-collagen I alpha 1 (ρ=0.6735, p=0.0164 and ρ=0.6295, p=0.0283, respectively). In healthy subjects, forced expiratory volume in 1 second (FEV1)/forced vital capacity demonstrated significant correlation with CC chemokine ligand 3 (CCL3)/macrophage inflammatory protein 1 alpha (ρ=0.3897 and p=0.0068). FEV1 exhibited significant correlation with CCL11/eotaxin (ρ=0.4445 and p=0.0017). Conclusion Inflammatory biomarkers in EBC might be useful to predict quality of life concerning respiratory symptoms and serologic markers. Further studies are needed

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

<p>Abstract</p> <p>Background</p> <p>Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.</p> <p>Methods</p> <p>We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.</p> <p>Results</p> <p>The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.</p> <p>Conclusions</p> <p>We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.</p

A logical network-based drug-screening platform for Alzheimer’s disease representing pathological features of human brain organoids

Developing effective drugs for Alzheimer’s disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood–brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs. © 2021, The Author(s).1

Clinical Features and Outcomes of Idiopathic Pulmonary Alveolar Proteinosis in Korean Population

Idiopathic pulmonary alveolar proteinosis (PAP) is a rare disorder in which lipoproteinaceous material accumulates within alveoli. There were few reports on Asian populations with idiopathic PAP. We retrospectively reviewed 38 patients with idiopathic PAP in Korea. We assessed clinical features, therapeutic efficacy and outcomes of whole lung lavage in patients with idiopathic PAP. The mean age at diagnosis was 52 yr. Eighty six percent of patients were symptomatic at diagnosis. Dyspnea and cough were the most common symptoms. Crackles were the most common physical examination finding. On pulmonary function test, a mild restrictive ventilatory defect was common, with a predicted mean forced vital capacity (FVC) of 77% and forced expiratory volume in one second (FEV1) of 84.6%. Diffusing capacity was disproportionately reduced at 67.7%. Arterial blood gas analysis revealed hypoxemia with a decreased PaO2 of 69.0 mmHg and an increased D(A-a)O2 of 34.2 mmHg. After whole lung lavage, PaO2, D(A-a)O2 and DLCO were significantly improved, but FVC and total lung capacity (TLC) were not different. This is the first multicenter study to analyze 38 Korean patients with idiopathic PAP. The clinical features and pulmonary parameters of Korean patients with idiopathic PAP are consistent with reports in other published studies. Whole lung lavage appears to be the most effective form of treatment

Interactions of CO2 with various functional molecules

The CO2 capturing and sequestration are of importance in environmental science. Understanding of the CO2-interactions with various functional molecules including multi-N-containing superbases and heteroaromatic ring systems is essential for designing novel materials to effectively capture the CO2 gas. These interactions are investigated using density functional theory (DFT) with dispersion correction and high level wave function theory (resolution-of-identity (RI) spin-component-scaling (scs) Moller-Plesset second-order perturbation theory (MP2) and coupled cluster with single, double and perturbative triple excitations (CCSD(T))). We found intriguing molecular systems of melamine, 1,5,7-triazabicyclo[4.4.0]dec-5- ene (TBD), 7-azaindole and guanidine, which show much stronger CO2 interactions than the well-known functional systems such as amines. In particular, melamine could be exploited to design novel materials to capture the CO2 gas, since one CO2 molecule can be coordinated by four melamine molecules, which gives a binding energy (BE) of similar to 85 kJ mol(-1), much larger than in other cases.open2

Systematic functional analysis of kinases in the fungal pathogen Cryptococcus neoformans

Cryptococcus neoformans is the leading cause of death by fungal meningoencephalitis; however, treatment options remain limited. Here we report the construction of 264 signature-tagged gene-deletion strains for 129 putative kinases, and examine their phenotypic traits under 30 distinct in vitro growth conditions and in two different hosts (insect larvae and mice). Clustering analysis of in vitro phenotypic traits indicates that several of these kinases have roles in known signalling pathways, and identifies hitherto uncharacterized signalling cascades. Virulence assays in the insect and mouse models provide evidence of pathogenicity-related roles for 63 kinases involved in the following biological categories: growth and cell cycle, nutrient metabolism, stress response and adaptation, cell signalling, cell polarity and morphology, vacuole trafficking, transfer RNA (tRNA) modification and other functions. Our study provides insights into the pathobiological signalling circuitry of C. neoformans and identifies potential anticryptococcal or antifungal drug targets.OAIID:RECH_ACHV_DSTSH_NO:T201615370RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A003535CITE_RATE:11.329FILENAME:4. ncomms12766.pdfDEPT_NM:농생명공학부EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/fce63c4a-7de7-4741-996f-d8d24af38905/linkCONFIRM:

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery