Objective:To observe the morphological changes of the sylvian fissure on the transthalamic section of fetal brain at 20-32 weeks, and grade the fetal sylvian fissure development by means of a simple scoring system and explore its clinical feasibility.Methods:From September 2018 to June 2020, 487 normal single fetuses of 20-32 weeks were examined in Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University. The sylvian fissure maturation was analyzed on the transthalamic section of fetal brain at 20-32 weeks and was graded from 0 to 5: un-visualized (grade 0), shallow arc (grade 1), obtuse-angled platform (grade 2), right-angled platform (grade 3), acute-angled platform (grade 4), and closed operculum (grade 5). The pregnancy outcomes and gestational age were recorded.Statistical analysis was performed by SPSS 20.0 software using box plot, Mann-Whitney U test, Weighted Kappa coefficient. Results:Left sylvian fissuer grades were obtained in 280 fetuses and right sylvian fissure grades were obtained in 247 fetuses. The fetal sylvian fissure maturation at 20-32 weeks was graded from 0 to 5, which increased with advancing gestation. Grade 0 only appeared in 3 fetuses at 20 weeks, and 99.4% fetuses at 20 weeks had grade ≥1. Grade 1 appeared in 20-22 weeks, grade 2 in 20-25 weeks, grade 3 in 22-26 weeks, grade 4 in 25-32 weeks, and grade 5 in 27-32 weeks. Box-plot and Mann-Whitney U test showed that gestational week distribution of sylvian fissure at all grades was symmetric on both sides ( P>0.05). The Weighted Kappa coefficients were 0.857(95% CI=0.750-0.957) and 0.939 (95% CI=0.859-1.000), respectively, with strong consistency regarding inter- and intra-observer agreements. Conclusions:Fetal sylvian fissure maturation at 20-32 weeks can be evaluated by means of a simple scoring system with symmetrical grading of both sides.

Objective:To assess the significance of counting the number of caudal vertebral ossification centers (OCN) below fetal terminal conus medullaris in the screening for closed spina bifida and tethered cord syndrome (TCS).Methods:The OCN was counted in 961 normal fetuses(normal group) between 17 and 41 gestational weeks and in 140 fetuses with closed spina bifida or tethered cord syndrome(abnormal group) from Jan.2013 to Dec.2020 in Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Women and Children′s Hospital, School of Medicine, Xiamen University and Maternity and Child Health Care of Guangxi Zhuang Autonomous Region. The OCN was counted in the dorsal mid-sagittal section of fetal caudal spine.The reliability and agreement test were evaluated by intraclass correlation coefficients in another 50 normal fetuses. The OCN was compared between two groups. ROC curve and the cut-off value were constructed and calculated.Results:In normal group, the N increased with the growing of gestational age.In the subgroup of 17-20 weeks, the OCN ranged from 5 to 7 in most fetuses. In the others subgroups, the OCN was equal to or greater than 6 in 99.9% cases and more than 6 in 97.1% cases. In abnormal group, OCN was less than 7 in 93.0% fetuses and less than 6 in 82.8% cases. There were statistical differences between the two groups except for the subgroup of 17-20 gestational weeks( P<0.05). With the cut-off value of 6.5, the specificity and sensitivity were 93.0% and 94.3% respectively for predicting the presence of closed spinal dysraphism or TCS. Conclusions:OCN is a simple way to evaluate the position of conus medullaris and to screen for the skin-covered spine dysraphism or TSC. OCN is more than 6 in most normal fetuses. Further evaluation of spine is required in fetuses with N less than or equal to 6.

Objective:To analyze the prognostic factors and evaluate the accuracy of existing survival prediction models in patients with lung cancer-derived spinal metastases who have undergone open surgery.Methods:According to the inclusion criteria, the data of 76 patients with spinal metastasis of lung cancer who underwent open surgery in the department of Orthopedics in Guangdong Provincial People's Hospital were collected from January 2019 to November 2021. The relationship between the number of bone metastasis, pathological type, visceral metastasis, epidermal growth factor receptor mutation, serum alkaline phosphatase (ALP), hemoglobin (Hb), Frankel grade and postoperative survival time in 76 cases was analyzed by Cox logical regression analysis and Kaplan-Meier method to determine the potential prognostic factors. The accuracy of Tomita score, Tokuhashi revised score, Katagiri New score, New England Spinal Metastasis Score score (NESMS) and Skeletal Oncology Research Group (SORG) machine learning algorithm in predicting postoperative survival time was verified by drawing receiver operating characteristic (ROC) curve.Results:The median follow-up time of the patients was 18.0 months (2.3-36.0 months). The median survival time was 12.6 months [95% CI (10.8, 14.4)]. The survival rates at 6 and 12 months after operation were 71.6% and 52.0%, respectively. Multivariate regression analysis showed that ALP [ HR=0.23, 95% CI (0.11, 0.48), P<0.001], Hb [ HR=4.48, 95% CI (2.07, 9.70), P< 0.001] and EGFR mutation [ HR=2.22, 95% CI (1.04, 4.76), P=0.040] were independent predictors of prognosis. The accuracy of Tomita score, Tokuhashi revised score (2005), Katagiri New score and NESMS score in predicting 1-year mortality was 58.7%, 65.7%, 70.5% and 65% respectively, and the accuracy in predicting 6-month mortality was 63.7%, 62.2%, 61.2% and 56.8% respectively. The accuracy of SORG machine learning algorithm in predicting 1-year and 90 d mortality was 81.1%, 67.5%, respectively. Conclusion:No EGFR mutation, ALP>164 U/L and Hb≤125 g/L were risk factors affecting the survival of patients with spinal metastasis of lung cancer. SORG machine learning algorithm has good accuracy in predicting the postoperative survival rate of patients with lung cancer spinal metastasis.

Lead poisoning severely threatens human health with its cumulation and durability in the body. The analysis of lead in blood is vital for screening, diagnosis, treatment, and prognostication of lead poisoning and for indirectly monitoring the level of lead in the environment. Although the detection programs are available throughout our country, the accuracy and comparability of the results cannot meet the expectation. A variety of factors can affect the accuracy of blood lead testing. To promote the application of blood lead analysis in clinical trials and reduce the bias of results, a better reference system for blood lead analysis should be established to evaluate the accuracy of traditional methods, promote the standardization of blood lead analysis and achieve accurate blood lead testing.

Objective : To study the effect of deep learning applied to the assisted diagnosis of radiolucent lesions and radiopaque lesions of the jaws in panoramic radiography and to reduce the missed diagnosis, with early screening to assist doctors to improve the diagnostic accuracy. Methods: This study was approved by the Ethics Committee of the West China Stomatological Hospital of Sichuan University. The YOLO v8m-p2 neural network model was constructed with 443 panoramic images as a subject to read. The labeled images were divided into 354 training sets, 45 verification sets, and 44 test sets, which were used for model training, verification, and testing. Accuracy, recall, F-1 score, G score, and mAP50 were used to evaluate the detection performance of the model. Results: 443 panoramic images covered the common benign lesions of the jaw, the number of radiolucent lesions of the jaw was 318, containing dentigerous cyst, odontogenic keratocyst, and ameloblastoma. The number of radiopaque lesions was 145, containing idiopathic osteosclerosis, odontoma, cementoma, and cemento-osseous dysplasia; the samples are well representative. The accuracy of the YOLO v8m-p2 neural network model in identifying jaw lesions was 0.887, and the recall, F-1 score, G score, and mAP50 were 0.860, 0.873, 0.873, and 0.863, respectively. The recall rates of dentigerous cyst, odontogenic keratocyst, and ameloblastoma were 0.833, 0.941, and 0.875, respectively. Conclusion YOLO v8m-p2 neural network model has good diagnostic performance in preliminary detection of radiolucent and radiopaque lesions of the jaws in panoramic radiography and multi-classification monitoring of radiolucent lesions of jaws, which can assist doctors to screen jaw diseases in panoramic radiography.