Purpose: The study is to describe the clinical characteristics, outcomes and follow-up of cancer patients with COVID-19. Methods: Clinical records, demographic data, signs and symptoms, laboratory results, cytokine pro les, chest CT scans, comorbidities, treatments, clinical outcomes, and RT-PCR of SARS-CoV-2 after discharge were retrospectively collected for fty-six cancer patients with laboratory-con rmed COVID-19 pneumonia who were admitted to

IMPORTANCE Health care workers (HCWs) have high infection risk owing to treating patients with coronavirus disease 2019 (COVID-19). However, research on their infection risk and clinical characteristics is limited. OBJECTIVES To explore infection risk and clinical characteristics of HCWs with COVID-19 and to discuss possible prevention measures.

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.

Background Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949–0·959), with a sensitivity of 0·923 (95% CI 0·914–0·932), specificity of 0·851 (0·842–0·860), a positive predictive value of 0·790 (0·777–0·803), and a negative predictive value of 0·948 (0·941–0·954). AI took a median of 0·55 min (IQR: 0·43–0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67–25·71) to draft a report and 23·06 min (15·67–39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947–1·000) and a specificity of 0·875 (95 %CI 0·833–0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718–0·940). Interpretation A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Non-invasive prediction of isocitrate dehydrogenase (IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.

Senescence of prostatic epithelial cells results in increased expression of IL-8, which can promote proliferation of non-senescent epithelial and stromal cells by direct and indirect mechanisms, and in this manner contributes to the increased tissue growth seen in BPH.

There has been little study of how the evolution of chemoresistance in cancer affects other aspects of disease pathogenesis. Here, we show that an important chemoresistance axis driven by cytidine deaminase (CDA) also acts to suppress cell-cycle progression by regulating cyclin E-CDK2 signaling. We found that CDA was regulated by miR-484 in a gemcitabine-resistant model of breast cancer. Elevating miR-484 expression reversed the CDA effects, thereby enhancing gemcitabine sensitivity, accelerating cell proliferation, and redistributing cell-cycle progression. Conversely, elevating CDA to restore its expression counteracted the chemosensitization and cell proliferative effects of miR-484. In clinical specimens of breast cancer, CDA expression was frequently downregulated and inversely correlated with miR-484 expression. Moreover, high expression of CDA was associated with prolonged disease-free survival in studied cohorts. Collectively, our findings established that miR-484-modulated CDA has a dual impact in promoting chemoresistance and suppressing cell proliferation in breast cancer, illustrating the pathogenic tradeoffs associated with the evolution of chemoresistance in this malignant disease. Cancer Res; 75(7); 1504-15. Ó2015 AACR.

Background and Purpose: Edaravone dexborneol, comprised of 2 active ingredients, edaravone and (+)-borneol, has been developed as a novel neuroprotective agent with synergistic effects of antioxidant and anti-inflammatory in animal models. The present clinical trial aimed at testing the effects of edaravone dexborneol versus edaravone on 90-day functional outcome in patients with acute ischemic stroke (AIS). Methods: A multicenter, randomized, double-blind, comparative, phase III clinical trial was conducted at 48 hospitals in China between May 2015 and December 2016. Inclusion criteria included patients diagnosed as AIS, 35 to 80 years of age, National Institutes of Health Stroke Scale Score between 4 and 24, and within 48 hours of AIS onset. AIS patients were randomized in 1:1 ratio into 2 treatment arms: 14-day infusion of edaravone dexborneol or edaravone injection. The primary end point was the proportion of patients with modified Rankin Scale score ≤1 on day 90 after randomization. Results: One thousand one hundred sixty-five AIS patients were randomly allocated to the edaravone dexborneol group (n=585) or the edaravone group (n=580). The edaravone dexborneol group showed significantly higher proportion of patients experiencing good functional outcomes on day 90 after randomization, compared with the edaravone group (modified Rankin Scale score ≤1, 67.18% versus 58.97%; odds ratio, 1.42 [95% CI, 1.12–1.81]; P =0.004). The prespecified subgroup analyses indicated that a greater benefit was observed in female patients than their male counterparts (2.26, 1.49–3.43 versus 1.14, 0.85–1.52). Conclusions: When edaravone dexborneol versus edaravone was administered within 48 hours after AIS, 90-day good functional outcomes favored the edaravone dexborneol group, especially in female patients. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02430350.