Introduction: Coronaviruses, both SARS-CoV and SARS-CoV-2, first appeared in China. They have certain biological, epidemiological and pathological similarities. To date, research has shown that their genes exhibit 79% of identical sequences and the receptor-binding domain structure is also very similar. There has been extensive research performed on SARS; however, the understanding of the pathophysiological impact of coronavirus disease 2019 (COVID-19) is still limited. Methods: This review drew upon the lessons learnt from SARS, in terms of epidemiology, clinical characteristics and pathogenesis, to further understand the features of COVID-19. Results: By comparing these two diseases, it found that COVID-19 has quicker and wider transmission, obvious family agglomeration, and higher morbidity and mortality. Newborns, asymptomatic children and normal chest imaging cases emerged in COVID-19 literature. Children starting with gastrointestinal symptoms may progress to severe conditions and newborns whose mothers are infected with COVID-19 could have severe complications. The laboratory test data showed that the percentage of neutrophils and the level of LDH is higher, and the number of CD4+ and CD8+T-cells is decreased in children's COVID-19 cases. Conclusion:Based on these early observations, as pediatricians, this review put forward some thoughts on children's COVID-19 and gave some recommendations to contain the disease.

Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Purpose: To test ribozymes targeting mouse telomerase RNA (mTER) for suppression of the progression of B16-F10 murine melanoma metastases in vivo.Experimental Design: Hammerhead ribozymes were designed to target mTER. The ribozyme sequences were cloned into a plasmid expression vector containing EBV genomic elements that substantially prolong expression of genes delivered in vivo. The activity of various antitelomerase ribozymes or control constructs was examined after i.v. injection of cationic liposome:DNA complexes containing control or ribozyme constructs. Expression of ribozymes and mTER at various time points were evaluated by quantitative real-time PCR. Telomerase activity was examined using the telomeric repeat amplification protocol.Results: Systemic administration of cationic liposome: DNA complexes containing a plasmid-expressed ribozyme specifically targeting a cleavage site at mTER nucleotide 180 significantly reduced the metastatic progression of B16-F10 murine melanoma. The antitumor activity of the anti-TER 180 ribozyme in mice was abolished by a single inactivating base mutation in the ribozyme catalytic core. The EBVbased expression plasmid produced sustained levels of ribozyme expression for the full duration of the antitumor studies. In addition to antitumor activity, cationic liposome: DNA complex-based ribozyme treatment also produced reductions in both TER levels and telomerase enzymatic activity in tumor-bearing mice.Conclusions: Systemic, plasmid-based ribozymes specifically targeting TER can reduce both telomerase activity and metastatic progression in tumor-bearing hosts. The work reported here demonstrates the potential utility of plasmid-based anti-TER ribozymes in the therapy of melanoma metastasis.

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Angiogenesis and osteogenesis are coupled. However, the cellular and molecular regulation of these processes remains to be further investigated. Both tissues have recently been recognized as endocrine organs, which has stimulated research interest in the screening and functional identification of novel paracrine factors from both tissues. This review aims to elaborate on the novelty and significance of endocrine regulatory loops between bone and the vasculature. In addition, research progress related to the bone vasculature, vessel-related skeletal diseases, pathological conditions, and angiogenesis-targeted therapeutic strategies are also summarized. With respect to future perspectives, new techniques such as single-cell sequencing, which can be used to show the cellular diversity and plasticity of both tissues, are facilitating progress in this field. Moreover, extracellular vesicle-mediated nuclear acid communication deserves further investigation. In conclusion, a deeper understanding of the cellular and molecular regulation of angiogenesis and osteogenesis coupling may offer an opportunity to identify new therapeutic targets.

Background: Cervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer. Methods: Data of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010-2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell's C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models. Result: Eleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model. Conclusion: A clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research.

Background Most studies have shown that maternal age is associated with birth weight. However, the specific relationship between each additional year of maternal age and birth weight remains unclear. The study aimed to analyze the specific association between maternal age and birth weight. Methods Raw data for all live births from 2015 to 2018 were obtained from the Medical Birth Registry of Xi’an, China. A total of 490,143 mother-child pairs with full-term singleton live births and the maternal age ranging from 20 to 40 years old were included in our study. Birth weight, gestational age, neonatal birth date, maternal birth date, residence and ethnicity were collected. Generalized additive model and two-piece wise linear regression model were used to analyze the specific relationships between maternal age and birth weight, risk of low birth weight, and risk of macrosomia. Results The relationships between maternal age and birth weight, risk of low birth weight, and risk of macrosomia were nonlinear. Birth weight increased 16.204 g per year when maternal age was less than 24 years old (95%CI: 14.323, 18.086), and increased 12.051 g per year when maternal age ranged from 24 to 34 years old (95%CI: 11.609, 12.493), then decreased 0.824 g per year (95% CI: -3.112, 1.464). The risk of low birth weight decreased with the increase of maternal age until 36 years old (OR = 0.917, 95%CI: 0.903, 0.932 when maternal age was younger than 27 years old; OR = 0.965, 95%CI: 0.955, 0.976 when maternal age ranged from 27 to 36 years old), then increased when maternal age was older than 36 years old (OR = 1.133, 95%CI: 1.026, 1.250). The risk of macrosomia increased with the increase of maternal age (OR = 1.102, 95%CI: 1.075, 1.129 when maternal age was younger than 24 years old; OR = 1.065, 95%CI: 1.060, 1.071 when maternal age ranged from 24 to 33 years old; OR = 1.029, 95%CI: 1.012, 1.046 when maternal age was older than 33 years old). Conclusions For women of childbearing age (20–40 years old), the threshold of maternal age on low birth weight was 36 years old, and the risk of macrosomia increased with the increase of maternal age.

BackgroundMaternal exposure to air pollution is related to fetal dysplasia. However, the association between maternal exposure to air pollution and the risk of congenital hypothyroidism (CH) in the offspring is largely unknown.MethodsWe conducted a national database based study in China to explore the association between these two parameters. The incidence of CH was collected from October 1, 2014 to October 1, 2015 from the Chinese Maternal and Child Health Surveillance Network. Considering that total period of pregnancy and consequently the total period of particle exposure is approximately 10 months, average exposure levels of PM2.5, PM10 and Air Quality Index (AQI) were collected from January 1, 2014 to January 1, 2015. Generalized additive model was used to evaluate the association between air pollution and the incidence of CH, and constructing receiver operating characteristic (ROC) curve was used to calculate the cut-off value.ResultsThe overall incidence of CH was 4.31 per 10,000 screened newborns in China from October 1, 2014 to October 1, 2015. For every increase of 1 μg/m3 in the PM2.5 exposure during gestation could increase the risk of CH (adjusted OR = 1.016 per 1 μg/m3 change, 95% CI, 1.001–1.031). But no significant associations were found with regard to PM10 (adjusted OR = 1.009, 95% CI, 0.996–1.018) or AQI (adjusted OR = 1.012, 95% CI,0.998–1.026) and the risk of CH in the offspring. The cut-off value of prenatal PM2.5 exposure for predicting the risk of CH in the offspring was 61.165 μg/m3.ConclusionsThe present study suggested that maternal exposure to PM2.5 may exhibit a positive association with increased risk of CH in the offspring. We also proposed a cut-off value of PM2.5 exposure that might determine reduction in the risk of CH in the offspring in highly polluted areas.