ObjectiveTo characterise the long-term outcomes of patients with COVID-19 admitted to a large New York City medical centre at 3 and 6 months after hospitalisation and describe their healthcare usage, symptoms, morbidity and mortality.DesignRetrospective cohort through manual chart review of the electronic medical record.SettingNewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical centre in New York City.ParticipantsThe first 1190 consecutive patients with symptoms of COVID-19 who presented to the hospital for care between 1 March and 8 April 2020 and tested positive for SARS-CoV-2 on reverse transcriptase PCR assay.Main outcome measuresType and frequency of follow-up encounters, self-reported symptoms, morbidity and mortality at 3 and 6 months after presentation, respectively; patient disposition information prior to admission, at discharge, and at 3 and 6 months after hospital presentation.ResultsOf the 1190 reviewed patients, 929 survived their initial hospitalisation and 261 died. Among survivors, 570 had follow-up encounters (488 at 3 months and 364 at 6 months). An additional 33 patients died in the follow-up period. In the first 3 months after admission, most encounters were telehealth visits (59%). Cardiopulmonary symptoms (35.7% and 28%), especially dyspnoea (22.1% and 15.9%), were the most common reported symptoms at 3-month and 6-month encounters, respectively. Additionally, a large number of patients reported generalised (26.4%) or neuropsychiatric (24.2%) symptoms 6 months after hospitalisation. Patients with severe COVID-19 were more likely to have reduced mobility, reduced independence or a new dialysis requirement in the 6 months after hospitalisation.ConclusionsPatients hospitalised with SARS-CoV-2 infection reported persistent symptoms up to 6 months after diagnosis. These results highlight the long-term morbidity of COVID-19 and its burden on patients and healthcare resources.
Although adipogenesis is mainly controlled by a small number of master transcription factors, including CCAAT/enhancer-binding protein family members and peroxisome proliferator-activated receptor ␥ (PPAR␥), other transcription factors also are involved in this process. Thyroid cancer cells expressing a paired box 8 (PAX8)-PPAR␥ fusion oncogene trans-differentiate into adipocyte-like cells in the presence of the PPAR␥ ligand pioglitazone, but this trans-differentiation is inhibited by the transcription factor NK2 homeobox 1 (NKX2-1). Here, we tested whether NKX family members may play a role also in normal adipogenesis. Using quantitative RT-PCR (RT-qPCR), we examined the expression of all 14 NKX family members during 3T3-L1 adipocyte differentiation. We found that most NKX members, including NKX2-1, are expressed at very low levels throughout differentiation. However, mRNA and protein expression of a related family member, NKX1-2, was induced during adipocyte differentiation. NKX1-2 also was up-regulated in cultured murine ear mesenchymal stem cells (EMSCs) during adipogenesis. Importantly, shRNA-mediated NKX1-2 knockdown in 3T3-L1 preadipocytes or EMSCs almost completely blocked adipocyte differentiation. Furthermore, NKX1-2 overexpression promoted differentiation of the ST2 bone marrowderived mesenchymal precursor cell line into adipocytes. Additional findings suggested that NKX1-2 promotes adipogenesis by inhibiting expression of the antiadipogenic protein COUP transcription factor II. Bone marrow mesenchymal precursor cells can differentiate into adipocytes or osteoblasts, and we found that NKX1-2 both promotes ST2 cell adipogenesis and inhibits their osteoblastogenic differentiation. These results support a role for NKX1-2 in promoting adipogenesis and possibly in regulating the balance between adipocyte and osteoblast differentiation of bone marrow mesenchymal precursor cells. The authors declare that they have no conflicts of interest with the contents of this article. This article contains Figs. S1 and S2 and Table S1.
Traditionally, mask defect analysis has been done through a visual inspection review. As the semiconductor industry moves into smaller process generations and the complexity of mask exponentially increases, the traditional mask defect analysis method becomes very time consuming. The Automatic Defect Severity Scoring (ADSS) module of i-Virtual Stepper System from Numerical Technologies offers an extremely fast and highly accurate software solution for defect printability analysis of advanced masks such as OPC and phase-shifting masks in a real production environment. In a previous paper [1], we have introduced the ADSS concept and discussed some results for line-space patterns on OPC and non-OPC masks. In this paper, we will discuss the ADSS results for both line-space and contact patterns on attenuated phaseshifting masks (ATTPSM), together with some ADSS results for line-space patterns on binary masks. The ADSS results are compared to wafer results. The wafer exposures were performed using 248 nm imaging technology and inspection images were generated on a KLA-Tencor's SLF27 system.
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