Objective:To explore the correlation between muscle CT measurement parameters, energy expenditure and acute exacerbation in patients with stable chronic obstructive pulmonary disease (COPD).Methods:The clinical data of 146 patients with stable COPD from March 2020 to November 2021 in Lu′an Hospital Affiliated to Anhui Medical University (Lu′an People′s Hospital) were retrospectively analyzed. The clinical data were recorded; the lung function was measured by bronchodilator test. The cross-sectional area and CT value of the pectoral muscle were measured by reconstructed CT images of the mediastinum; the total energy consumption was calculated by Weir formula. Acute exacerbations within 3 and 12 months were recorded. Multivariate Logistic regression was used to analyze the independent risk factors for acute exacerbation in patients with stable COPD. The receiver operating characteristic (ROC) curve was used to analyze the efficacy of total energy expenditure, pectoral muscle cross-sectional area and pectoral muscle CT value for predicting acute exacerbation in patients with stable COPD.Results:Among 146 patients with stable COPD, 38 cases (26.03%) developed acute exacerbation within 3 months (acute exacerbation group), and 108 cases (73.97%) did not develop acute exacerbation (non-acute exacerbation group). The proportion of age<60 years old, rate of acute exacerbation within 12 months and rate of pulmonary function grading Ⅲ to Ⅳ in acute exacerbation group were significantly higher than those in non-acute exacerbation group: 71.05% (27/38) vs. 47.22% (51/108), 52.63% (20/38) vs. 30.56% (33/108) and 63.16% (24/38) vs. 37.96% (41/108), the total energy consumption, pectoral muscle cross-sectional area and pectoral muscle CT value were significantly lower than those in non-acute exacerbation group: (2 036.28 ± 163.13) J/d vs. (2 389.59 ± 204.71) J/d, (28.79 ± 3.45) cm 2 vs. (31.61 ± 4.56) cm 2 and (29.79 ± 3.06) HU vs. (34.52 ± 4.38) HU, and there were statistical differences ( P<0.05 or <0.01). Multivariate Logistic regression analysis result showed that age ≥60 years old, lower total energy expenditure, smaller pectoral muscle cross-sectional area and lower pectoral muscle CT value were independent risk factors for acute exacerbation in patients with stable COPD ( OR = 26.493, 1.015, 1.245 and 1.437; 95% CI 3.745 to 187.405, 1.008 to 1.022, 1.002 to 1.546 and 1.109 to 1.861; P<0.01 or <0.05). The ROC curve analysis result showed that combined prediction of the total energy consumption, pectoral muscle cross-sectional area and pectoral muscle CT value for acute exacerbation in patients with stable COPD had the largest area under the curve (0.962), with a sensitivity of 86.1%, a specificity of 80.8%, and the optimal cutoff values of 2 206.12 J/d, 32.39 cm 2 and 31.63 HU, respectively. Conclusions:The elderly age, smaller pectoral muscle cross-sectional area, lower pectoral muscle CT value and lower total energy expenditure are independent risk factors for acute exacerbation in patients with stable COPD. The combination of pectoral muscle cross-sectional area, pectoral muscle CT value and total energy expenditure has a good predictive effect on the risk of acute exacerbation in patients with stable COPD, and relevant indexes can be paid attention to in clinical treatment.

Complex structural variants(CSVs)are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants.How-ever,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy.As a result,there has been limited progress for CSV discovery com-pared with simple structural variants.Here,we systematically analyzed the multi-breakpoint con-nection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read sequencing.Specifically,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined models.Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms.Notably,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70％,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,respectively.Moreover,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed duplication.Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs.Mako is publicly available at https://github.com/xjtu-omics/Mako.

We aimed to develop a whole-genome sequencing(WGS)-based copy number variant(CNV)calling algorithm with the potential of replacing chromosomal microarray assay(CMA)for clinical diagnosis.JAX-CNV is thus developed for CNV detection from WGS data.The perfor-mance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and com-pared to the 112 CNVs reported by clinically validated CMAs for these 31 samples.The result showed that JAX-CNV recalled 100％of these CNVs.Besides,JAX-CNV identified an average of 30 CNVs per individual,representing an approximately seven-fold increase compared to calls of clinically validated CMAs.Experimental validation of 24 randomly selected CNVs showed one false positive,i.e.,a false discovery rate(FDR)of 4.17％.A robustness test on lower-coverage data revealed a 100％sensitivity for CNVs larger than 300 kb(the current threshold for College of American Pathologists)down to 10×coverage.For CNVs larger than 50 kb,sensi-tivities were 100％for coverages deeper than 20×,97％for 15×,and 95％for 10×.We developed a WGS-based CNV pipeline,including this newly developed CNV caller JAX-CNV,and found it capable of detecting CMA-reported CNVs at a sensitivity of 100％with about a FDR of 4％.We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS.JAX-CNV is available at https://github.com/The J acksonLaboratory/JAX-CNV.