Objective To analyze the metabolic profile in serum between normal and orthotopic xenograft nude mice burdened with three human pancreatic cancer cell lines,which were differentiated differently.Methods Human pancreatic cancer lines SW1990,BxPC-3 and Panc-1 were subcutaneously injected into the nude mice,respectively.When the tumor volume reached 1.0 cm3,the nude mice were euthanized and the tumor tissues were removed and implanted to the pancreas to establish the orthotopic xenograft mice model.The serum from three orthotopic xenograft tumor nude mice and the normal controls were collected and then analyzed by 1H nuclear magnetic resonance spectroscopy.Results The three orthotopic xenograft nude mice models were successfully established.In SW1990,BxPC-3 and Panc-1 group,the orthotopic xenograft tumor formation rate was 79％ (11/14),93％ (13/14) and 86％ (12/14),while the mortality was 7％ (1/14),0 and 7％ (1/14),respectively.Compared with control group,the content of metabolites in the serum of orthotopic xenograft tumor nude mice was increased including creatine,alanine,glutamine,1-methylhistidine,isoleucine,lactate,phenylalanine,tryptophan and valine,but the glycerolphosphocholine (GPC) and glucose levels were reduced.As the tumors progressed to be more malignant,the content of valine and isoleucine tended to increase.Conclusions The establishment of the orthotopic implantation tumor nude mice model was stable and reliable with high tumor formation rate.Obvious metabolic differences of glucose,lipid and amino acids were observed between normal and human pancreatic cancer tumor burdening nude mice models.The common metabolic features identified in all three nude mice models burdened with human pancreatic cancer could be used as the potential markers for diagnosing human pancreatic cancer.

Objective:To investigate the clinical phenotype and gene mutation in a child with developmental disorders caused by CTNNB1 gene mutation. Methods:Clinical data of a child with CTNNB1 gene mutation who was admitted to Xiamen Hospital of Fudan University Affiliated Pediatric Hospital in May 2017 were collected, whole exome sequencing technology was applied to verify the family lineage of the child, and the pathogenicity of mutation site was analyzed. Results:The patient was a 6 years and 1 month old male, with a clinical phenotype including mental retardation, motor developmental disorders, speech disorders, visual disorders (internal strabismus), microcephaly, and behavioral problems (social withdrawal, overdependence, etc.), as well as panic syndrome (i.e., sudden shrieking in response to auditory and visual stimuli, extensional rigidity of the body, etc., followed by short periods of general extensional rigidity). The whole exome sequencing results showed the presence of a de novo mutation c.283(exon4)C>T in the CTNNB1 gene, and the c.283(exon4)C>T mutation was interpreted as pathogenic (PVS1+PS2+PS1+PM2+PM) according to the American College of Medical Genetics and Genomics variant classification criteria and guidelines. No relevant genetic variants were found in the parental family verification. Conclusion:CTNNB1 gene mutation c.283(exon4)C>T can cause neurodevelopmental disorders, including mental retardation, motor developmental disorders, speech disorders, visual disorders, microcephaly and behavioral abnormalities.

Schinzel-Giedion syndrome caused by SETBP1 gene mutation is a rare neurodevelopmental disorder characterized by neurodevelopmental disorders, multi-organ congenital developmental abnormalities (such as skeletal anomalies, urinary and renal malformations, heart defects, etc.), and an increased risk of childhood cancer. The clinical data and diagnosis and treatment process of a patient with Schinzel-Giedion syndrome related neurodevelopmental disorders caused by SETBP1 gene mutation were reported in this article. The clinical characteristics of the disease were analyzed through literature review to improve clinical doctors′ understanding of the disease.

Mycobacterium tuberculosis is the causative agent of tuberculosis(TB),which is still the leading cause of mortality from a single infectious disease worldwide.The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M.tuberculosis.Here,we harnessed an endogenous type Ⅲ-A CRISPR/Cas10 system of M.tuberculosis for efficient gene editing and RNA interference(RNAi).This simple and easy method only needs to transform a single mini-CRISPR array plasmid,thus avoiding the introduction of exogenous protein and minimizing proteotoxicity.We demonstrated that M.tuberculosis genes can be efficiently and specifically knocked in/out by this system as con-firmed by DNA high-throughput sequencing.This system was further applied to single-and multiple-gene RNAi.Moreover,we successfully performed genome-wide RNAi screening to identify M.tuberculosis genes regulating in vitro and intracellular growth.This system can be extensively used for exploring the functional genomics of M.tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

The coronavirus disease 2019 (COVID-19) has caused a global pandemic. All people including children are generally susceptible to COVID-19, but the condition is relatively mild for children. The diagnosis of COVID-19 is largely based on the epidemiological evidence and clinical manifestations, and confirmed by positive detection of virus nucleic acid in respiratory samples. The main symptoms of COVID-19 in children are fever and cough; the total number of white blood cell count is usually normal or decreased; the chest imaging is characterized by interstitial pneumonia, which is similar to other respiratory virus infections and infections. Early identification, early isolation, early diagnosis and early treatment are important for clinical management. The treatment of mild or moderate type of child COVID-19 is mainly symptomatic. For severe and critical ill cases, the oxygen therapy, antiviral drugs, antibacterial drugs, glucocorticoids, mechanical ventilation or even extracorporeal membrane oxygenation (ECMO) may be adopted, and the treatment plan should be adjusted timely through multi-disciplinary cooperation.
Betacoronavirus ; isolation & purification ; Child ; Coronavirus Infections ; diagnosis ; pathology ; therapy ; Humans ; Pandemics ; Pneumonia, Viral ; diagnosis ; diagnostic imaging ; etiology ; pathology ; therapy

Betacoronavirus ; isolation & purification ; Child ; Clinical Laboratory Techniques ; Coronavirus Infections ; diagnosis ; drug therapy ; therapy ; Humans ; Pneumonia, Viral ; diagnosis ; therapy