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ObjectiveAbnormal resting-state functional connectivity (FC), particularly in the default mode network (DMN) and the salience network (SN), has been reported in schizophrenia, but little is known about the effects of antipsychotics on these networks. The purpose of this study was to examine the effects of atypical antipsychotics on DMN and SN and the relationship between these effects and symptom improvement in patients with schizophrenia.MethodsThis was a prospective study of 33 patients diagnosed with schizophrenia and treated with antipsychotics at Shanghai Mental Health Center. Thirty-three healthy controls matched for age and gender were recruited. All subjects underwent functional magnetic resonance imaging (fMRI). Healthy controls were scanned only once; patients were scanned before and after 6–8 weeks of treatment.ResultsIn the DMN, the patients exhibited increased FC after treatment in the right superior temporal gyrus, right medial frontal gyrus, and left superior frontal gyrus and decreased FC in the right posterior cingulate/precuneus (P<0.005). In the SN, the patients exhibited decreased FC in the right cerebellum anterior lobe and left insula (P<0.005). The FC in the right posterior cingulate/precuneus in the DMN negatively correlated with the difference between the Clinical Global Impression (CGI) score pre/post-treatment (r=−0.564, P=0.023) and negative trends with the difference in the Positive and Negative Syndrome Scale (PANSS) total score pre/post-treatment (r=−0.475, P=0.063) and the difference in PANSS-positive symptom scores (r=−0.481, P=0.060).ConclusionThese findings suggest that atypical antipsychotics could regulate the FC of certain key brain regions within the DMN in early-phase schizophrenia, which might be related to symptom improvement. However, the effects of atypical antipsychotics on SN are less clear.

Currently, the prevention and control of COVID-19 outside Hubei province in China, and other countries has become more and more critically serious. We developed and validated a diagnosis aid model without CT images for early identification of suspected COVID-19 pneumonia (S-COVID-19-P) on admission in adult fever patients and made the validated model available via an online triage calculator. Patients admitted from Jan 14 to Feb 26, 2020 with the epidemiological history of exposure to COVID-19 were included [Model development (n = 132) and validation (n = 32)]. Candidate features included clinical symptoms, routine laboratory tests and other clinical information on admission. Features selection and model development were based on Lasso regression. The primary outcome is the development and validation of a diagnosis aid model for S-COVID-19-P early identification on admission. The development cohort contains 26 S-COVID-19-P and 7 confirmed COVID-19 pneumonia cases. The model performance in held-out testing set and validation cohort resulted in AUCs of 0.841 and 0.938, F-1 score of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and the precision of 0.400 and 0.500. Based on this model, an optimized strategy for S-COVID-19-P early identification in fever clinics has also been designed. S-COVID-19-P could be identified early by a machine-learning model only used collected clinical information without CT images on admission in fever clinics with 100% recall score. The well performed and validated model has been deployed as an online triage tool, which is available at: https://intensivecare.shinyapps.io/COVID19/.

Technical developments and improved access to neuroimaging techniques has brought us closer to understanding the neuropathological origins of schizophrenia. Using data-driven disease progression modelling on cross-sectional MRIs from 1124 schizophrenia patients, we characterize two distinct but stable 'trajectories' of brain atrophy, separately beginning in the Broca's area (subtype1) and the hippocampus (subtype2). The two 'trajectories' are replicated in cross-validation samples. Individuals within each subtype are further classified into two stages ('pre-atrophy' and 'post-atrophy'). These subtypes show different atrophy patterns and symptom profiles. Longitudinal data from 523 schizophrenia patients treated by antipsychotics only (APM) or adjunct transcranial magnetic stimulation (TMS) reveal that APM effects relate to phenotypic subtype (more effective in the subtype1) while TMS effects relate to the stage (superior outcomes in the 'pre-atrophy' stage). These findings suggest distinct pathophysiological processes underlying schizophrenia that potentially yield to stratification and prognostication -a key requirement for personalizing treatments in enduring illnesses. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2000041106).