Original Article

ネコ32匹について,10% urethane-1% chloralose(5ml/kg)の腹腔内麻酔を行ない,横隔膜運動神経を機能的単一神経発射としてとり出し,その基本的特性と換気機構との相関および横隔膜神経支配について検討した。1)横隔膜運動神経線維について,機能的単一神経発射115本の発射パターンを自発呼吸下における気速曲線と対比し,I〜IV型に分類した。2)横隔膜神経におけるこれらの線維の構成は,I型40%,II型48%,III型10%そしてIV型は2%以下であった。3)I・II・III型の線維の基本的特性は,気道抵抗を加えた場合および呼気反射における発射パターンの変化からI型は相動性の要素を有し,II・III型は緊張性の要素を有している。4)これらの線維と換気機構の相関をみると,I型の線維は,主として効果的な吸気を瞬間的に行なうためにdynamicであり,II型の線維は,静的状態における吸気に主役を演じstaticである。さらに初期の呼出を円滑に行なわせるのにおもな役割を果たしている。III型は,主として腹圧に拮抗した緊張性の線維と考えられる。IV型については,ガンマ-運動線維である可能性について考察した。6)横隔膜筋支配については,腰部はI型,肋骨部は,I・II・III型の線維によって支配されていると考えられる。32 cats weighing 1.8 to 3.4 kg were intraperitoneally anesthetized with 10% urethane and 1% chloralose (5 ml/kg). 115 functionally unitary discharges of the phrenic nerve classified into four different types. Type I fibres initiate their firings after the beginning of the inspiratory phase and cease their activities prior to the onset of the expiratory flow change. Type II fibres fire with the very onset of the inspiratory phase and extend their activites untill the initial part of the expiration. The firings of Type III fibres dominate the whole inspiratory phase and initial two thirds of the expiration. Type IV showed continuously tonic firing throughout the whole respiratory cycle and increase their activities slightly during the inspiratory phase. Type I and II occupied more than 80% of all pattern. Modification of firing mode of each fibre type was observed under spontaneous respiration with air-way resistance and deflation reflex. The results may indicate characteristic features of each fibre type. Type I fibres may contribute to the kinetic contraction of the diaphragm and Type II the tonic one. Type III fibres were more tonic than Type II and may keep diaphragmatic tension against the intra-abdominal pressure. Type IV fibres probably belong to gamma motoneurons. Electromyographic observations suggested dominant innervation of Type I fibres to the pars cruralis. Pars costalis was mainly innervated by Type II fibres

Mass flow rate measurement of pneumatically conveyed solids in a square-shaped pipe through multi-sensor fusion and data-driven modelling

Online continuous measurement of the mass flow rate of pneumatically conveyed solids in a square-shaped pipe is desirable for monitoring and optimizing industrial processes. However, existing techniques using a single type of sensor have limitations in measuring the mass flow rate of solids because of the complexity of the dynamics of solids flow due to the four sharp corners of a square-shaped pipe. This paper proposes a multi-sensor fusion and data-driven modelling-based method to tackle this challenge. A multi-sensor system based on acoustic, capacitive, and electrostatic sensing principles is designed and implemented to obtain the sound pressure level in the flow, volumetric concentration of solids, and solids velocity, respectively. Simultaneously, a range of statistical features is obtained by performing time-domain, frequency-domain, and time-frequency domain analyses on all sensor signals. The statistical features reflecting the variation of the mass flow rate of solids, as well as solids velocity and volume concentration of solids, are then fed into a data-driven model. A data-driven model based on a combined convolutional neural network and long short-term memory (CNN-LSTM) network is established, and its performance is compared with those of the back-propagation artificial neural network, support vector machine, CNN, and LSTM models. Experimental tests were conducted on a laboratory-scale rig on both horizontal and vertical pipelines to train and evaluate the CNN-LSTM model with solids velocity ranging from 11 to 23 m/s and the mass flow rate of solids from 8 to 26 kg/h. The CNN-LSTM model outperforms all other models with a relative error within ±1% under all test conditions

Pharmacokinetics of single- and multiple-dose flumatinib in patients with chronic phase chronic myeloid leukemia

IntroductionFlumatinib is a novel, oral breakpoint cluster region-abelson (BCR-ABL) tyrosine kinase inhibitor that has demonstrated manageable safety and promising efficacy in patients with newly diagnosed chronic phase (CP) chronic myeloid leukemia (CML). MethodsThis study evaluated the pharmacokinetic (PK) profiles of flumatinib mesylate tablets at a dose of 400 mg and 600 mg in patients with CML-CP. The study was registered at chictr.org Identifier (ChiCTR2100044700). In this open-label, pharmacokinetic study, eligible patients were administered a single-dose of flumatinib 400 mg or 600 mg on day 1, followed by 2-day washout and 8 consecutive days of once-daily administration. Serial plasma samples were assayed for flumatinib and its metabolites (N-demethylate metabolite M1 and amide-bond hydrolytic metabolite M3).ResultsTwenty-nine patients were assigned to flumatinib 400 mg (n=14) or 600 mg (n=15). Serum concentrations of flumatinib reached maximum measured plasma concentration (Cmax) at a median time of 2 hours after each single dose, and then eliminated slowly with a mean apparent terminal disposition half-life (t1/2) from 16.0 to 16.9 hours. Following single- and multiple-dose administration, flumatinib exposure (Cmax, area under the concentration-time curve from 0 to t hours (AUC0-t), area under the concentration-time curve from 0 hours to infinity (AUC0-∞)) increased in an approximately dose-proportional manner. There was approximately 4.1- and 3.4- fold drug accumulation at steady-state after multiple-dose administration at 400 mg and 600 mg, respectively. The drug-related AEs associated with both treatments were primarily low-grade and tolerable events.ConclusionAnalysis of PK parameters indicated that flumatinib exposure increased in an approximately dose-proportional manner. Further research needs to be conducted in a large sample-size study

Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China

Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences

Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival

Genetic intratumor heterogeneity is associated with tumor occurrence, development and overall outcome. The present study aims to explore the association between mutant-allele fraction (MAF) heterogeneity and patient overall survival in lung cancer. Somatic mutation data of 939 non-small cell lung cancer (NSCLC) cases were obtained from The Cancer Genome Atlas. Entropy-based mutation allele fraction (EMAF) score was used to describe the uncertainty of individual somatic mutation patterns and to further analyze the association with patient overall survival. Results indicated that association between EMAF and overall survival was significant in the discovery set [hazard ratio (H)R=1.62; 95% confidence interval (CI): 1.08–2.41; P=0.018] and replication set (HR=1.63; 95% CI: 1.11–2.37; P=0.011). In addition, EMAF was also significantly different in lung adenocarcinoma and squamous cell carcinoma. Furthermore, a significant difference was indicated in early-stage patients. Results from c-index analysis indicated that EMAF improved the model predictive performance on the 3-year survival beyond that of traditional clinical staging, particularly in early-stage patients. In conclusion, EMAF successfully reflected MAF heterogeneity among patients with NSCLC. Additionally, EMAF improved the predictive performance in early-stage patient prognosis beyond that of traditional clinical staging. In clinical application, EMAF appears to identify a subset of early-stage patients with a poor prognosis and therefore may help inform clinical decisions regarding the application of chemotherapy after surgery

Epigenetic modifications in KDM lysine demethylases associate with survival of early-stage NSCLC

BACKGROUND: KDM lysine demethylase family members are related to lung cancer clinical outcomes and are potential biomarkers for chemotherapeutics. However, little is known about epigenetic alterations in KDM genes and their roles in lung cancer survival. METHODS: Tumor tissue samples of 1230 early-stage non-small cell lung cancer (NSCLC) patients were collected from the five independent cohorts. The 393 methylation sites in KDM genes were extracted from epigenome-wide datasets and analyzed by weighted random forest (Ranger) in discovery phase and validation dataset, respectively. The variable importance scores (VIS) for the sites in top 5% of both discovery and validation sets were carried forward for Cox regression to further evaluate the association with patient's overall survival. TCGA transcriptomic data were used to evaluate the correlation with the corresponding DNA methylation. RESULTS: DNA methylation at sites cg11637544 in KDM2A and cg26662347 in KDM1A were in the top 5% of VIS in both discovery phase and validation for squamous cell carcinomas (SCC), which were also significantly associated with SCC survival (HRcg11637544 = 1.32, 95%CI, 1.16-1.50, P = 1.1 × 10-4; HRcg26662347 = 1.88, 95%CI, 1.37-2.60, P = 3.7 × 10-3), and correlated with corresponding gene expression (cg11637544 for KDM2A, P = 1.3 × 10-10; cg26662347 for KDM1A P = 1.5 × 10-5). In addition, by using flexible criteria for Ranger analysis followed by survival classification tree analysis, we identified four clusters for adenocarcinomas and five clusters for squamous cell carcinomas which showed a considerable difference of clinical outcomes with statistical significance. CONCLUSIONS: These findings highlight the association between somatic DNA methylation in KDM genes and early-stage NSCLC patient survival, which may reveal potential epigenetic therapeutic targets

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer

B-cell translocation gene 2 (BTG2) is a tumour suppressor protein known to be downregulated in several types of cancer. In this study, we investigated a potential role for BTG2 in early-stage non-small cell lung cancer (NSCLC) survival. We analysed BTG2 methylation data from 1230 early-stage NSCLC patients from five international cohorts, as well as gene expression data from 3038 lung cancer cases from multiple cohorts. Three CpG probes (cg01798157, cg06373167, cg23371584) that detected BTG2 hypermethylation in tumour tissues were associated with lower overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51-2.21] and the independent validation set (HR=1.85). In the expression analysis, BTG2 expression was positively correlated with survival in each cohort (HR range, 0.28-0.68), which we confirmed with meta-analysis (HR=0.61, 95% CI 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Importantly, an integrative model of BTG2 methylation, expression and clinical information showed better predictive ability in the training set and validation set. In conclusion, the methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk