The beginning of the twenty-rst century has been marked by three distinct waves of zoonotic coronavirus outbreaks into the human population. The current pandemic COVID-19 (Coronavirus disease 2019) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With a rapid infection rate, it is a global threat endangering the livelihoods of millions worldwide. Currently, and despite the collaborative efforts of governments, researchers, and the pharmaceutical industries, there are no substantially signi cant treatment protocols for the disease. To address the need for such an immediate call of action, we leveraged the largest dataset of drug-induced transcriptomic perturbations, public SARS-CoV-2 transcriptomic datasets, and expression pro les from normal lung transcriptomes. Our unbiased systems biology approach not only shed light on previously unexplored molecular details of SARS-CoV-2 infection (e.g., interferon signaling, in ammation and ACE2 co-expression hallmarks in normal and infected lungs) but most importantly prioritized more than 50 repurposable drug candidates (e.g., Corticosteroids, Janus kinase and Bruton kinase inhibitors). Further clinical investigation of these FDA approved candidates as monotherapy or in combination with an antiviral regimen (e.g., Remdesivir) could lead to promising outcomes in COVID-19 patients.

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PurposeLatent autoimmune diabetes in adults (LADA) is a slowly progressing form of immune-mediated diabetes that combines phenotypical features of both type 2 diabetes mellitus (T2DM) and type 1 diabetes mellitus (T1DM), meaning that accurate and early diagnosis of this subtype of diabetes is critical for optimal long-term management. Urinary C-peptide creatinine ratio (UCPCR) represents a non-invasive and practical method for assessing endogenous insulin production to facilitate diabetes classification. However, no study to date has reported the use of UCPCR in identifying LADA.Patients and methodsA total of 574 subjects were included in our study (42 LADA, 61 T1DM, 471 T2DM). All participants were evaluated for UCPCR and underwent clinical and laboratory evaluations. UCPCR was compared among different subtypes of diabetes using multinomial regression analysis, and a receiver operating characteristic (ROC) curve was used to identify its performance in diagnosing LADA.ResultsUCPCR was lower in LADA (0.4±0.6 nmol/mmol) compared with T2DM (1.2±0.9 nmol/mmol), but higher than in T1DM (0.2±0.3 nmol/mmol) (p<0.05). The association between UCPCR and LADA remained significant after adjusting for gender, age, age at diagnosis, body mass index, high-density lipoprotein cholesterol, and triglyceride (OR, 95% confidence interval (CI), 0.29 (0.09, 0.95)). The ROC curve revealed an area under the curve of 0.835 (95% CI (0.742–0.928), p<0.001). The cut-off point for UCPCR ≤ 0.46 nmol/mmol was 82.1% for sensitivity and 76.7% for specificity in the diagnosis of LADA.ConclusionUCPCR may represent a non-invasive, simple, and practical measurement of insulin secretion for early discrimination of LADA in routine clinical practice.

Pulmonary sclerosing hemangiomas (PSH) of the lung are uncommon tumors and may present cytological atypia with unusual manifestations. The development of PSH combined with other different tumors in lung is extremely rare. We report a case of coexistence of PSH and primary adenocarcinoma in a young female occurring in the same pulmonary nodular mass of right lower lobe. The solitary mass of lung was well-circumscribed on chest computed tomography (CT) and gross examination. Histologically, the mass contained two separated portions and displayed typically histological features of PSH and acinar adenocarcinoma, respectively. In PSH portion, the tumor was composed of sheets of round cells with scattered surface cuboidal cells forming small tubules. Both round and surface cells were diffusely positive for epithelial membrane antigen (EMA) and thyroid transcription factor-1 (TTF-1), but lack immunoreactivity for pancytokeratin in round cells. In adenocarcinoma portion, the tumor cells formed irregular-shaped glands with cytologically malignant cells infiltrating in fibroblastic stroma, and no TTF-1-positive round cells could be observed in this portion. Under the microscopy, there was no gradual transition of these two portions observed in mass. A diagnosis of PSH combined with primary adenocarcinoma of lung was made. There was no evidence of tumor recurrence during the period of postoperative 6-month follow-up. To our knowledge, this is the first case of coexistence of PSH and adenocarcinoma in the same nodule of lung. In addition, the biological behavior and histological differential diagnosis of this tumor were also discussed.

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The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.