The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

BackgroundIn the ongoing COVID-19 pandemic, the susceptibility of patients with rheumatic diseases to COVID-19 remains unclear. We aimed to investigate susceptibility to COVID-19 in patients with autoimmune rheumatic diseases during the ongoing COVID-19 pandemic. MethodsWe did a multicentre retrospective study of patients with autoimmune rheumatic diseases in Hubei province, the epicentre of the COVID-19 outbreak in China. Patients with rheumatic diseases were contacted through an automated telephone-based survey to investigate their susceptibility to COVID-19. Data about COVID-19 exposure or diagnosis were collected. Families with a documented history of COVID-19 exposure, as defined by having at least one family member diagnosed with COVID-19, were followed up by medical professionals to obtain detailed information, including sex, age, smoking history, past medical history, use of medications, and information related to COVID-19. FindingsBetween March 20 and March 30, 2020, 6228 patients with autoimmune rheumatic diseases were included in the study. The overall rate of COVID-19 in patients with an autoimmune rheumatic disease in our study population was 0•43% (27 of 6228 patients). We identified 42 families in which COVID-19 was diagnosed between Dec 20, 2019, and March 20, 2020, in either patients with a rheumatic disease or in a family member residing at the same physical address during the outbreak. Within these 42 families, COVID-19 was diagnosed in 27 (63%) of 43 patients with a rheumatic disease and in 28 (34%) of 83 of their family members with no rheumatic disease (adjusted odds ratio [OR] 2•68 [95% CI 1•14-6•27]; p=0•023). Patients with rheumatic disease who were taking hydroxychloroquine had a lower risk of COVID-19 infection than patients taking other disease-modifying anti-rheumatic drugs (OR 0•09 [95% CI 0•01-0•94]; p=0•044). Additionally, the risk of COVID-19 was increased with age (adjusted OR 1•04 [95% CI 1•01-1•06]; p=0•0081).Interpretation Patients with autoimmune rheumatic disease might be more susceptible to COVID-19 infection than the general population.

BackgroundThoracic surgeries including thoracotomy and VATS are some of the highest risk procedures that often lead to CPSP, with or without a neuropathic component. This retrospective study aims to determine retrospectively the prevalence of CPSP following thoracic surgery, its predicting risk factors, the incidence of neuropathic component, and its impact on quality of life.MethodsPatients who underwent thoracic surgeries including thoracotomy and VATS between 01/2010 and 12/2011 at the First Affiliated Hospital, School of Medicine, Zhejiang University were first contacted and screened for CPSP following thoracic surgery via phone interview. Patients who developed CPSP were then mailed with a battery of questionnaires, including a questionnaire referenced to Maguire's research, a validated Chinese version of the ID pain questionnaire, and a SF-36 Health Survey. Logistic regression analyses were subsequently performed to identify risk factors for CPSP following thoracic surgery and its neuropathic component.ResultsThe point prevalence of CPSP following thoracic surgery was 24.9% (320/1284 patients), and the point prevalence of neuropathic component of CPSP was 32.5% (86/265 patients). CPSP following thoracic surgery did not improve significantly with time. Multiple predictive factors were identified for CPSP following thoracic surgery, including age<60 years old, female gender, prolonged duration of post-operative chest tube drainage (≥4 days), options of post-operative pain management, and pre-existing hypertension. Furthermore, patients who experienced CPSP following thoracic surgery were found to have significantly decreased physical function and worse quality of life, especially those with neuropathic component.ConclusionsOur study demonstrated that nearly 1 out of 4 patients underwent thoracic surgery might develop CPSP, and one third of them accompanied with a neuropathic component. Early prevention as well as aggressive treatment is important for patients with CPSP following thoracic surgery to achieve a high quality of life.

When developing a real-time energy management strategy for a plug-in hybrid electric vehicle, it is still a challenge for the Equivalent Consumption Minimum Strategy to achieve near-optimal energy consumption, because the optimal equivalence factor is not readily available without the trip information. With the help of realistic speeding profiles sampled from a plug-in hybrid electric bus running on a fixed commuting line, this paper proposes a convenient and effective approach of determining the equivalence factor for an adaptive Equivalent Consumption Minimum Strategy. Firstly, with the adaptive law based on the feedback of battery SOC, the equivalence factor is described as a combination of the major component and tuning component. In particular, the major part defined as a constant is applied to the inherent consistency of regular speeding profiles, while the second part including a proportional and integral term can slightly tune the equivalence factor to satisfy the disparity of daily running cycles. Moreover, Pontryagin's Minimum Principle is employed and solved by using the shooting method to capture the co-state dynamics, in which the Secant method is introduced to adjust the initial co-state value. And then the initial co-state value in last shooting is taken as the optimal stable constant of equivalence factor. Finally, altogether ten successive driving profiles are selected with different initial SOC levels to evaluate the proposed method, and the results demonstrate the excellent fuel economy compared with the dynamic programming and PMP method.

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Previous neuroimaging studies have suggested similar neural activations for word reading in native and second languages. However, such similarities were qualitatively determined (i.e., overlapping activation based on traditional univariate activation analysis). In this study, using representational similarity analysis and an artificial language training paradigm, we quantitatively computed cross-language neural pattern similarity to examine the modulatory effect of proficiency in the new language. Twenty-four native Chinese speakers were trained to learn 30 words in a logographic artificial language for 12 days and scanned while performing a semantic decision task after 4-day training and after 12-day training. Results showed that higher proficiency in the new language was associated with higher cross-language pattern similarity in select regions of the reading network.

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