Objective:To develop an artificial intelligence (AI) quality control model of fetal heart in the first trimester and verify its effectiveness.Methods:A total of 18 694 images of the four-chamber view(4CV) and three-vessel and tracheal view(3VT) of fetal heart in the first trimester were selected from Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University since January 2022 to December 2022. A total of 14 432 images were manually annotated. The one-stage target detection algorithm YOLO V5 was used to train the AI quality control model in the first trimester of fetal heart, and 4 262 images (golden standard set by expert group) were used to evaluate the application effectiveness of AI quality control model. Kappa consistency test was used to compare the results of section classification and standard degree judgment from AI quality control model, Doctor 1(D1) and Doctor 2(D2).Results:①Precision of the AI quality control model was 0.895, recall was 0.852, mean average precision (mAP 50) was 0.873.The average precision(AP) of the AI quality control model for section classification was 0.907 (4CV) and 0.989 (3VT), respectively. ②Compared with the gold standard, the overall coincidence rate and consistency of section classification of AI quality control model, D1 and D2 were 99.91% (Kappa=0.998), 100% (Kappa=1.000), 100% (Kappa=1.000), respectively. The coincidence rate and consistency of the plane standard degree evaluation from the AI quality control model, D1 and D2 were 97.46% (Weighted Kappa=0.932), 93.73% (Weighted Kappa=0.847), and 93.12% (Weighted Kappa=0.832), respectively. Strong consistency was displayed. Moreover, AI quality control model showed the highest coincidence rate and the strongest consistency in judging section standard degree, which was superior to manual quality control. The time-consuming of AI quality control (0.012 s/sheet) was significantly less than the way of manual quality control (4.76-6.11 s/sheet)( Z=-8.079, P<0.001). Conclusions:The use of artificial intelligent fetal heart quality control model in the first trimester can effectively and accurately control the image quality.

To analyze the differentially expressed proteins which interacted with NF-kappaB in the uterine lower segment smooth muscle tissues under different status of labor onset, and to provide a new foundation on the mechanisms for labor onset.
 Methods: NF-κB P65 protein expression in smooth muscle tissues from the term non-labor group, natural term labor group and drug-induced term labor group was analyzed by Western blot. Co-immunoprecipitation and SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) were performed to detect the proteins interacting with NF-κB p65 in the NF-κB p65 complexes. The components of the complex were identified by LC-ESI-MS/MS (liquid chromatography-tandem electrospray mass spectrometry) and database analysis. The identified differentially expressed proteins were confirmed by Western blot.
 Results: Positive expression of NF-κB was detected in all of the three groups. 10 differentially expressed proteins were identified by LC-ESI-MS/MS in human lower segment myometrium tissues in the term non-labor group and natural term labor group, mean while, 5 differentially expressed proteins were identified in the term non-labor group and the drug-induced labor group. 3 differential expression proteins were detected in all of the 3 groups, including Heat shock 70, Annexin A6 and Desmin, which were verified by Western blot. These proteins were mainly involved in chaperone, signal transduction, cell structure, and energy metabolism process, respectively.
 Conclusion: NF-κB expressed in uterine smooth muscle cells is involved in the process of initiation and regulation of labor onset through a number of proteins relevant to signal transduction, cell structure and energy metabolism.
Blotting, Western ; Electrophoresis, Polyacrylamide Gel ; Energy Metabolism ; genetics ; Female ; Humans ; Immunoprecipitation ; Labor, Obstetric ; genetics ; Molecular Chaperones ; genetics ; Myocytes, Smooth Muscle ; Myometrium ; physiology ; NF-kappa B ; genetics ; physiology ; Pregnancy ; Protein Interaction Mapping ; Proteomics ; Signal Transduction ; genetics ; Tandem Mass Spectrometry ; Transcription Factor RelA

OBJECTIVE: To identify the screening time and prepare a screening schedule for outpatients with acute fatty liver of pregnancy (AFLP).
 METHODS: AFLP patients who admitted to Xiangya Hospital and the Second Xiangya Hospital, Central South University, Hunan, China between November, 2006 and December, 2013, were retrospectively studied. The diagnosis of 78 AFLP patients met the domestic clinical and laboratory criteria and the Swansea criteria. Clinical and laboratory data obtained on admission were used for analysis. Contrastive analysis was conducted within our data and other large medical centers or general hospitals. 
 RESULTS: The difference between domestic clinical and laboratory criteria and Swansea criteria in diagnosing AFLP patients in the 2 hospitals mentioned above was significant (P<0.05). The maternal mortality was 14.10% (11/78) and perinatal mortality was 17.95 % (14/78). The mean gestational age at delivery was 35.6 weeks. Based on the clinical and laboratory data, more than 85% of AFLP patients showed abnormal levels of transaminase, bilirubin, and white blood cells, as well as coagulation dysfunction. Gastrointestinal symptoms, such as abdominal pain and vomiting, jaundice, renal impairment and ascites or bright liver on ultrasound scan, were showed in 50%-85% of AFLP patients. Less than 50% of patients suffered from low blood sugar, high blood ammonia or hepatic encephalopathy.
 CONCLUSION The 34th gestation week might be important time for screening AFLP outpatients. Gastrointestinal symptoms, blood routine, liver function, and coagulant function tests are recommended as the first grade screening indicators. Renal function, blood sugar test, and abdominal ultrasound could be the second grade screening indicators for AFLP outpatients.
China ; Fatty Liver ; diagnosis ; Female ; Gestational Age ; Humans ; Mass Screening ; methods ; Outpatients ; Pregnancy ; Pregnancy Complications ; diagnosis ; Retrospective Studies ; Time Factors