Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. METHODS: The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve. RESULTS: CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively. CONCLUSIONS: The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.

Spinal Nrf2 translocation may inhibit neuronal NF-κB activation and alleviate allodynia in a rat model of bone cancer pain.

Bone cancer pain (BCP) is a clinical pathology that urgently needs to be solved, but research on the mechanism of BCP has so far achieved limited success. Nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) has been shown to be involved in pain, but its involvement in BCP and the specific mechanism have yet to be examined. This study aimed to test the hypothesis that BCP induces the transfer of Nrf2 from the cytoplasm to the nucleus and further promotes nuclear transcription to activate heme oxygenase-1 (HO-1) and inhibit the activation of nuclear factor-kappa B (NF-κB) signalling, ultimately regulating the neuroinflammatory response. Von-Frey was used for behavioural analysis in rats with BCP, whereas western blotting, real-time quantitative PCR (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) were used to detect molecular expression changes, and immunofluorescence was used to detect cellular localization. We demonstrated that BCP induced increased Nrf2 nuclear protein expression with decreased cytoplasmic protein expression in the spinal cord. Further increases in Nrf2 nuclear protein expression can alleviate hyperalgesia and activate HO-1 to inhibit the expression of NF-κB nuclear protein and inflammatory factors. Strikingly, intrathecal administration of the corresponding siRNA reversed the above effects. In addition, the results of double immune labelling revealed that Nrf2 and NF-κB were coexpressed in spinal cord neurons of rats with BCP. In summary, these findings suggest that the entry of Nrf2 into the nucleus promotes the expression of HO-1, inhibiting activation of the NF-κB signalling pathway, reducing neuroinflammation and ultimately exerting an anti-nociceptive effect.

Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.

Since the first report of SARS-CoV-2 virus in Wuhan, China in December 2019, a global outbreak of Corona Virus Disease 2019 (COVID-19) pandemic has been aroused. In the prevention of this disease, accurate diagnosis of COVID-19 is the center of the problem. However, due to the limitation of detection technology, the test results are impossible to be totally free from pseudo-positive or -negative. Improving the precision of the test results asks for the identification of more biomarkers for COVID-19. On the basis of the expression data of COVID-19 positive and negative samples, we first screened the feature genes through ReliefF, minimal-redundancy-maximum-relevancy, and Boruta_MCFS methods. Thereafter, 36 optimal feature genes were selected through incremental feature selection method based on the random forest classifier, and the enriched biological functions and signaling pathways were revealed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Also, protein-protein interaction network analysis was performed on these feature genes, and the enriched biological functions and signaling pathways of main submodules were analyzed. In addition, whether these 36 feature genes could effectively distinguish positive samples from the negative ones was verified by dimensionality reduction analysis. According to the results, we inferred that the 36 feature genes selected via Boruta_MCFS could be deemed as biomarkers in COVID-19.

LncRNA EBLN3P attributes methotrexate resistance in osteosarcoma cells through miR-200a-3p/O-GlcNAc transferase pathway.

BACKGROUND: Osteosarcoma is highly malignant. The migration, invasion, and chemoresistance contribute to poor prognosis of osteosarcoma. Research reported that endogenous bornavirus-like nucleoprotein 3 pseudogene (EBLN3P) promotes the progression of osteosarcoma. METHODS: In this study, the expression of EBLN3P in osteosarcoma tissue with different methotrexate (MTX) treatment responses was measured. Osteosarcoma cell lines with MTX resistance were constructed, and bioinformatic analysis was performed to explore the potential involved targets and pathways. RESULTS: Higher EBLN3P was associated with MTX resistance. Downregulation of LncEBLN3P decreased the MTX resistance of osteosarcoma cells by sponging miR-200a-3p, an important microRNA that affects epithelial-mesenchymal transition (EMT). The decreased miR-200a-3p resulted in the upregulation of its target gene O-GlcNAc transferase (OGT), which in turn promoted the EMT process of osteosarcoma cells. Further analysis confirmed that the loss of OGT and over-expression of miR-200a-3p could partly abolish the MTX resistance induced by LncEBLN3P. CONCLUSION: LncEBLN3P is upregulated in osteosarcoma and increases the MTX resistance in osteosarcoma cells through downregulating miR-200a-3p, which in turn promoted the EMT process of osteosarcoma cells by increasing the OGT.

Effectiveness of telemedicine for cardiovascular disease management: systematic review and meta-analysis.

BACKGROUND: With the development of information communication technology (ICT), telemedicine has become a promising option for patients with chronic diseases who need continuous monitoring at home or in remote health care facilities. As cardiovascular disease (CVD) is responsible for an estimated 17.9 million deaths globally each year, it is appropriate to evaluate the effectiveness of telemedicine for the health care management of CVD patients. METHODS: The Library of Congress, LISTA (EBSCO), PubMed (NLM), and Web of Science databases were searched with a date limitation from 1 January 2000 until 5 August 2021 for Randomized Controlled Trial (RCT) studies. Two independent researchers screened the records for inclusion and extracted the data for synthesis, supported by RevMan 5.0 software. As one of the clinical outcomes, the mean difference and standard deviation of systolic blood pressure were synthesized. For the Quality-of-life measures, EuroQol-5D (EQ-5D) scores were also synthesized and for the depression level, CESD-10 scores were synthesized. RESULTS: We identified 23 studies for qualitative analysis and 21 studies for quantitative analysis. 21 studies included systolic blood pressure as an outcome measure and the results show a statistical difference (P<0.05) between the intervention group and the control group and a favorable inclination toward the Telemedicine enhanced health care program over the usual care. Of the six studies that included body mass index (BMI) as an outcome measure, there was no significant difference in BMI between the telemedicine and usual health care groups. A total of five studies assessed patients' quality of life using the EQ-5D instrument. After excluding one study following the sensitivity analysis, telemedicine was shown to significantly improve patients' quality of life. Three studies that investigated patients' mental health (CSE-D-10) also showed a significantly beneficial effect of telemedicine over usual health care. DISCUSSION: This review found limited evidence to support some of the outcomes in the original study designs. Overall, our findings suggested a favorable effect of telemedicine intervention in the field of health care for CVD patients. Due to the unavoidable heterogeneity within the selected literature, a more detailed investigation and analysis of the unclear outcomes is recommended.

Value of imaging findings in predicting post-operative recurrence of desmoid-type fibromatosis.

Desmoid-type fibromatosis is a rare type of soft-tissue tumor originating from connective tissue of the fascia or aponeurosis, which exhibits aggressive growth, high likelihood of relapse and less frequent distant metastasis. The present study aimed to predict the recurrence rate and time by retrospectively analyzing the clinical data (sex, age and recurrence time), imaging findings [tumor location, maximum diameter, border, computed tomography (CT) enhancement ratio, magnetic resonance enhancement ratio and T2 signal ratio] and pathological features (Ki-67 and microscopic margin) in a total of 102 cases of pathologically confirmed desmoid-type fibromatosis. The risk ratio of each factor was calculated using the Cox proportional hazards regression model and the cumulative recurrence-free survival rate was determined using the Kaplan-Meier method and the log-rank test. The cohort comprised of 73 females and 29 males, with mean age of 32.86±12.64 years (range, 6-78 years). The 1-year and 2-year recurrence rate was 31 and 54%, respectively. The median age at recurrence was 29 years. Univariate analysis indicated that sex, maximum tumor diameter, CT enhancement ratio and Ki-67 had a significant effect on the recurrence time. Furthermore, multivariate analysis revealed that sex, maximum tumor diameter, Ki-67 and T2 signal ratio were independently associated with the time of recurrence, and the risk ratios were 0.424, 1.100, 1.084 and 1.268, respectively. Therefore, in male patients with a larger maximum tumor diameter, positivity for Ki-67 and a higher T2 signal ratio, desmoid-type fibromatosis was more likely to recur after surgery.