Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Highlights d SARS-CoV-2 genome sequencing and phylogenetic analyses identify 35 recurrent mutations d Association with 117 clinical phenotypes reveals potentially important mutations d D500-532 in Nsp1 coding region correlates with lower viral load and serum IFN-b d Viral isolates with D500-532 mutation induce lower IFN-I response in the infected cells

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We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV -mutated non-small cell lung cancer (NSCLC) patients. A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV -mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts. The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit ( = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model ( < 0.0001). The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. .

Background: Computed tomography (CT)-guided transthoracic needle biopsy is a well-established, minimally invasive diagnostic tool for pulmonary lesions. Few large studies have been conducted on the diagnostic performance and adequacy for molecular testing of transthoracic core needle biopsy (TCNB) for small pulmonary lesions.Methods: This study included CT-guided TCNB with 18-gauge cutting needles in 560 consecutive patients with small (≤3 cm) pulmonary lesions from January 2012 to January 2015. There were 323 males and 237 females, aged 51.8±12.7 years. The size of the pulmonary lesions was 1.8±0.6 cm. The sensitivity, specificity, accuracy and complications of the biopsies were investigated. The risk factors of diagnostic failure were assessed using univariate and multivariate analyses. The sample's adequacy for molecular testing of non-small cell lung cancer (NSCLC) was analyzed.Results: The overall sensitivity, specificity, and accuracy for diagnosis of malignancy were 92.0% (311/338), 98.6% (219/222), and 94.6% (530/560), respectively. The incidence of bleeding complications was 22.9% (128/560), and the incidence of pneumothorax was 10.4% (58/560). Logistic multivariate regression analysis showed that the independent risk factors for diagnostic failure were a lesion size ≤1 cm [odds ratio (OR), 3.95; P=0.007], lower lobe lesions (OR, 2.83; P=0.001), and pneumothorax (OR, 1.98; P=0.004). Genetic analysis was successfully performed on 95.45% (168/176) of specimens diagnosed as NSCLC. At least 96.8% of samples with two or more passes from a lesion were sufficient for molecular testing. Conclusions:The diagnostic yield of small pulmonary lesions by CT-guided TCNB is high, and the procedure is relatively safe. A lesion size ≤1 cm, lower lobe lesions, and pneumothorax are independent risk factors for biopsy diagnostic failure. TCNB specimens could provide adequate tissues for molecular testing.