Clinical characteristics of fever clinic visits during the coronavirus disease 2019 pandemic: a retrospective case-control study.

Background: Little is known about the change in characteristics of fever-clinic visits during the coronavirus disease 2019 (COVID-19) pandemic. We sought to examine the changes in the volume, characteristics, and outcomes of patients presenting at a fever clinic duringclinic during the first-level response to COVID-19. Methods: We conducted a single tertiary-center retrospective case-control study. We included consecutive patients aged 14 years or older who visited the fever clinic of a tertiary hospital during the period of the first-level response to the COVID-19 pandemic in Fuzhou, China (from 24 January to 26 February 2020). We also analyzed the data of patients in the same period of 2019 as a control. We compared a number of outcome measures, including the fever clinic volumes, consultation length, proportion of patients with pneumonia, hospital admission rate, and in-hospital mortality, using the fever-clinic visit data during the two periods. Results: We included 1,013 participants [median age: 35; interquartile range (IQR): 27-50, 48.7% male] in this retrospective study, including 707 in 2020 and 306 in 2019. The median daily number of participants who presented at the fever clinic in 2020 was significantly higher than that in 2019 [18 (IQR: 15-22) vs. 13 (IQR: 8-17), P=0.001]. Participants in 2020 had a longer consultation length than those in 2019 [127 (IQR: 51-204) vs. 20 (IQR: 1-60) min, P<0.001]. Participants in 2020 were also more likely to be diagnosed with acute pneumonia than those in 2019 [168 (23.8%) vs. 40 (13.1%), P<0.001]. The hospital admission rate in 2020 was higher than in 2019 [73 (10.3%) vs. 13 (4.2%), P=0.001]. No significant difference was found in terms of the in-hospital mortality of participants in 2020 and 2019 [8 (1.1%) vs. 0, P=0.114]. Conclusions: Our findings suggest a higher visits volume, proportion of acute pneumonia, and hospital admission rate among patients presenting at fever clinic during the COVID-19 pandemic. Improved measures need to be implemented.

Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013-2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient's enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733-0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696-0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: - 3.133, P = 0.008), maximum tumor diameter (Z value: - 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659-0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

Retroperitoneal dendritic cell sarcoma: A case report.

RATIOANLE: Interdigitating dendritic cell sarcoma (IDCS) is a rare sarcoma that originates from interdigitating dendritic cells in lymphoid tissue, the imaging characteristics of which are poorly defined. Pathological examination can identify the tumor, but reports on the imaging characteristics of IDCS are limited. PATIENT CONCERNS: Here, we report a case of IDCS in a 48-year-old female involving the retroperitoneal area. The patient had a lumbar mass on her right lower back for 4 years, and which started increasing in size 1 year before. DIAGNOSES: An irregular soft tissue mass (10.1cm × 8.5 cm in size) in the right lower back of retroperitoneum was detected by CT examination with unclear borders, uneven density, and necrosis. The solid components of the mass were significantly enhanced on postcontrast imaging. The soft tissue was irregular and uneven. Cystic solid masses were observed on MRI examination in the right retroperitoneum, lateral abdominal wall, waist, and back. Necrosis, hemorrhage, and cystic transformation were observed inside the lesion. The cyst wall, separation, and wall nodules were significantly enhanced on the postcontrast image. No distant metastasis was observed. Postoperative pathology confirmed the diagnosis of IDCS. INTERVENTIONS: The patient underwent surgical resection. The resected margin was positive, and the patient received adjuvant radiotherapy 2 months after the surgery. OUTCOMES: Twelve months after radiotherapy, the patient's chest CT showed multiple metastases in both lungs. The patient was started on combination chemotherapy of doxorubicin and ifosfamide, and the follow-up is still ongoing. LESSONS: Imaging provides a unique advantage to determine the extent of the IDCS, the invasion of adjacent tissues, and the presence or absence of distant metastases.

Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative.

BACKGROUND: Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge. OBJECTIVE: To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice. METHODS: A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated. RESULTS: A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60]. CONCLUSION: The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.

Long non-coding RNA PCAT-1 promotes tumor progression by inhibiting miR-129-5p in human ovarian cancer.

INTRODUCTION: Ovarian cancer (OC) is one of the most common malignancies and the leading cause of cancer-related death among women. The long non-coding RNA Prostate cancer-associated transcript-1 (PCAT-1) has been reported to play important roles in multiple human cancers. However, the role of PCAT-1 in OC has never been investigated. The purpose of this study was to investigate the expression and roles of PCAT-1 in OC. MATERIAL AND METHODS: Expression of PCAT-1 and miR-129-5p in OC tissues and cell lines was determined by qRT-PCR. Cell proliferation and apoptosis were analyzed by MTT assay and flow cytometry, respectively. The interaction between PCAT-1 and miR-129-5p was demonstrated by luciferase reporter assay. RESULTS: PCAT-1 is significantly upregulated in OC tissues and cell lines (p < 0.05). Overexpression of PCAT-1 promotes proliferation of OC cells and inhibits their apoptosis (p < 0.05). In addition, miR-129-5p is markedly downregulated in OC and its level is inversely correlated with PCAT-1 expression in OC tumor tissues (p < 0.05). miR-129-5p inhibits proliferation and induces apoptosis in OC cell lines (p < 0.05). Furthermore, it has been demonstrated that miR-129-5p is directly targeted by PCAT-1 and miR-129-5p overexpression can effectively attenuate the effects of PCAT-1 on the proliferation and apoptosis of OC cells. CONCLUSIONS: Our results suggest that PCAT-1 functions as an oncogene by inhibiting miR-129-5p in OC and silencing PCAT-1 may be a novel therapeutic strategy in the treatment of OC.