FINDINGS: We present a draft genome assembly that includes 200 Gb of Illumina reads, 4 Gb of Moleculo synthetic long reads, and 108 Gb of Chicago libraries, with a final size matching the estimated genome size of 2.7 Gb, and a scaffold N50 of 4.8 Mb. We also present an alternative assembly including 27 Gb raw reads generated using the Pacific Biosciences platform. In addition, we sequenced the proteome of the same individual and RNA from 3 different tissue types from 3 other species of squid (Onychoteuthis banksii, Dosidicus gigas, and Sthenoteuthis oualaniensis) to assist genome annotation. We annotated 33,406 protein-coding genes supported by evidence, and the genome completeness estimated by BUSCO reached 92%. Repetitive regions cover 49.17% of the genome.
CONCLUSIONS: This annotated draft genome of A. dux provides a critical resource to investigate the unique traits of this species, including its gigantism and key adaptations to deep-sea environments.
METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
METHODS: For this systematic review and meta-analysis, we searched PubMed, Embase, Scopus, and the Cochrane Library from inception to May 1, 2019, for relevant original research articles without any language restrictions. The literature search and data extraction were done independently by two investigators. Primary outcomes were the prevalence of non-obese or lean people within the NAFLD group and the prevalence of non-obese or lean NAFLD in the general, non-obese, and lean populations; the incidence of NAFLD among non-obese and lean populations; and long-term outcomes of non-obese people with NAFLD. We also aimed to characterise the demographic, clinical, and histological characteristics of individuals with non-obese NAFLD.
FINDINGS: We identified 93 studies (n=10 576 383) from 24 countries or areas: 84 studies (n=10 530 308) were used for the prevalence analysis, five (n=9121) were used for the incidence analysis, and eight (n=36 954) were used for the outcomes analysis. Within the NAFLD population, 19·2% (95% CI 15·9-23·0) of people were lean and 40·8% (36·6-45·1) were non-obese. The prevalence of non-obese NAFLD in the general population varied from 25% or lower in some countries (eg, Malaysia and Pakistan) to higher than 50% in others (eg, Austria, Mexico, and Sweden). In the general population (comprising individuals with and without NAFLD), 12·1% (95% CI 9·3-15·6) of people had non-obese NAFLD and 5·1% (3·7-7·0) had lean NAFLD. The incidence of NAFLD in the non-obese population (without NAFLD at baseline) was 24·6 (95% CI 13·4-39·2) per 1000 person-years. Among people with non-obese or lean NALFD, 39·0% (95% CI 24·1-56·3) had non-alcoholic steatohepatitis, 29·2% (21·9-37·9) had significant fibrosis (stage ≥2), and 3·2% (1·5-5·7) had cirrhosis. Among the non-obese or lean NAFLD population, the incidence of all-cause mortality was 12·1 (95% CI 0·5-38·8) per 1000 person-years, that for liver-related mortality was 4·1 (1·9-7·1) per 1000 person-years, cardiovascular-related mortality was 4·0 (0·1-14·9) per 1000 person-years, new-onset diabetes was 12·6 (8·0-18·3) per 1000 person-years, new-onset cardiovascular disease was 18·7 (9·2-31·2) per 1000 person-years, and new-onset hypertension was 56·1 (38·5-77·0) per 1000 person-years. Most analyses were characterised by high heterogeneity.
INTERPRETATION: Overall, around 40% of the global NAFLD population was classified as non-obese and almost a fifth was lean. Both non-obese and lean groups had substantial long-term liver and non-liver comorbidities. These findings suggest that obesity should not be the sole criterion for NAFLD screening. Moreover, clinical trials of treatments for NAFLD should include participants across all body-mass index ranges.
FUNDING: None.
PATIENTS AND METHODS: Patients ≥18 years old with histologically/cytologically confirmed stage IIIB/IV EGFR mutation-positive NSCLC and Eastern Cooperative Oncology Group performance status 0-2 were randomized 1:1 to receive erlotinib (oral; 150 mg once daily until progression/unacceptable toxicity) or GP [G 1250 mg/m(2) i.v. days 1 and 8 (3-weekly cycle); P 75 mg/m(2) i.v. day 1, (3-weekly cycle) for up to four cycles]. Primary end point: investigator-assessed progression-free survival (PFS). Other end points include objective response rate (ORR), overall survival (OS), and safety.
RESULTS: A total of 217 patients were randomized: 110 to erlotinib and 107 to GP. Investigator-assessed median PFS was 11.0 months versus 5.5 months, erlotinib versus GP, respectively [hazard ratio (HR), 0.34, 95% confidence interval (CI) 0.22-0.51; log-rank P < 0.0001]. Independent Review Committee-assessed median PFS was consistent (HR, 0.42). Median OS was 26.3 versus 25.5 months, erlotinib versus GP, respectively (HR, 0.91, 95% CI 0.63-1.31; log-rank P = .607). ORR was 62.7% for erlotinib and 33.6% for GP. Treatment-related serious adverse events (AEs) occurred in 2.7% versus 10.6% of erlotinib and GP patients, respectively. The most common grade ≥3 AEs were rash (6.4%) with erlotinib, and neutropenia (25.0%), leukopenia (14.4%), and anemia (12.5%) with GP.
CONCLUSION: These analyses demonstrate that first-line erlotinib provides a statistically significant improvement in PFS versus GP in Asian patients with EGFR mutation-positive NSCLC (NCT01342965).
METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.
RESULTS: 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).
CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.