Avian trichomoniasis caused by Trichomonas gallinae is a serious protozoan disease worldwide. The domestic pigeon (Columba livia domestica) is the main host for T. gallinae and plays an important role in the spread of the disease. Based on the internal transcribed spacers of nuclear ribosomal DNA of this parasite, a pair of primers (TgF2/TgR2) was designed and used to develop a PCR assay for the diagnosis of T. gallinae infection in domestic pigeons. This approach allowed the identification of T. gallinae, and no amplicons were produced when using DNA from other common avian pathogens. The minimum amount of DNA detectable by the specific PCR assay developed in this study was 15 pg. Clinical samples from Guangzhou, China, were examined using this PCR assay and a standard microscopy method, and their molecular characteristics were determined by phylogenetic analysis. All of the T. gallinae-positive samples detected by microscopic examination were also detected as positive by the PCR assay. Most of the samples identified as negative by microscopic examination were detected as T. gallinae positive by the PCR assay and were confirmed by sequencing. The positive samples of T. gallinae collected from Guangzhou, China, were identified as T. gallinae genotype B by sequencing and phylogenetic analyses, providing relevant data for studying the ecology and population genetic structures of trichomonads and for the prevention and control of the diseases they cause.
China* ; Columbidae* ; Diagnosis ; DNA ; DNA, Ribosomal ; Ecology ; Genetic Structures ; Genotype ; Methods ; Microscopy ; Parasites ; Polymerase Chain Reaction* ; Trichomonas*

Humans ; Benzodiazepines ; Hypnotics and Sedatives/therapeutic use* ; Intensive Care Units ; Midazolam

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.

OBJECTIVES: To investigate the incidence of extrauterine growth retardation (EUGR) and its risk factors in very preterm infants (VPIs) during hospitalization in China. METHODS: A prospective multicenter study was performed on the medical data of 2 514 VPIs who were hospitalized in the department of neonatology in 28 hospitals from 7 areas of China between September 2019 and December 2020. According to the presence or absence of EUGR based on the evaluation of body weight at the corrected gestational age of 36 weeks or at discharge, the VPIs were classified to two groups: EUGR group (n=1 189) and non-EUGR (n=1 325). The clinical features were compared between the two groups, and the incidence of EUGR and risk factors for EUGR were examined. RESULTS: The incidence of EUGR was 47.30% (1 189/2 514) evaluated by weight. The multivariate logistic regression analysis showed that higher weight growth velocity after regaining birth weight and higher cumulative calorie intake during the first week of hospitalization were protective factors against EUGR (P<0.05), while small-for-gestational-age birth, prolonged time to the initiation of total enteral feeding, prolonged cumulative fasting time, lower breast milk intake before starting human milk fortifiers, prolonged time to the initiation of full fortified feeding, and moderate-to-severe bronchopulmonary dysplasia were risk factors for EUGR (P<0.05). CONCLUSIONS It is crucial to reduce the incidence of EUGR by achieving total enteral feeding as early as possible, strengthening breastfeeding, increasing calorie intake in the first week after birth, improving the velocity of weight gain, and preventing moderate-severe bronchopulmonary dysplasia in VPIs.
Female ; Fetal Growth Retardation ; Gestational Age ; Hospitalization ; Humans ; Incidence ; Infant ; Infant, Newborn ; Infant, Premature ; Infant, Very Low Birth Weight ; Prospective Studies ; Risk Factors