OBJECTIVE To provide the clinical anatomy data of the lingual artery for clinical application concerning about the lingual artery. METHODS CTA examination of the carotid artery was performed in 80 adult volunteers. The 3D reconstruction images of the carotid artery with hyoid bone were obtained by using 64-slices spiral CT. At the same time, 20 extraoral dissections of the submandibular region were performed on 10 human cadavers. The origin, pathway, and anatomic relations of the lingual artery in CTA and cadavers were studied. The distance from the origin of the lingual artery to the bifurcation of the common carotid artery and tip of the greater horn of hyoid bone were separately measured, and the distance between the segment of the greater horn of hyoid bone of lingual artery and the middle of greater horn of hyoid bone were also measured. And the contrast analysis was carried on the result. RESULTS The lingual artery was mainly come from the external carotid artery (74.4 % in the CTA; 80% in the Cadavers), the next origin was come from the facial artery (23.1 % in the CTA; 20 % in the Cadavers), and it was few to found that the lingual artery had origin in the superior thyroid artery, which was 2.5 % in CTA and none in Cadavers. The path of the lingual artery had high variation, but the position between the segment of the greater horn of hyoid bone of lingual artery and the greater horn of hyoid bone had constancy relatively. The lingual artery run forward with approximation parallel to the greater horn of hyoid bone into tongue,and which located in thesuperior to the greater horn of hyoid bone (2.32?1.29 mm) or in the inferior to the greater horn of hyoid bone (2.00?1.68 mm). The distances from the origin of the lingual artery to the bifurcation of the common carotid artery and tip of the greater horn of hyoid bone in CTA and cadavers were (12.93?7.36) mm, (10.40?5.75) mmand (14.80?6.18) mm, (8.35?5.44) mm respectively. CONCLUSION The lingual artery can be show clearly in CTA and can get the anatomy data in physiological condition with CTA.

0.05).The Nestin and C-kit expressions can be detected in all cases of GISTs by RT-PCR and Western Blot.Conclusion In conjunction with C-kit,Nestin may be a useful marker for diagnosis of GISTs but it cannot be used as a tumor differentiation index.

OBJECTIVE: To provide the clinical anatomy data in physiological condition of the lingual artery in 64-slices spiral computed tomography angiography (CTA) for clinical treatment concerning the lingual artery. METHOD: CTA examination of the carotid artery were performed on 80 adult volunteers. The 3D reconstruction images of the carotid artery with hyoid bone were obtained by using 64-slices spiral CT and image postprocessing workstation. The origin, pathway, and anatomic relations of the lingual artery in CTA were studied. The distance from the origin of the lingual artery to the bifurcation of the common carotid artery and tip of the greater horn of hyoid bone were separately measured, and the distance between the segment of the greater horn of hyoid bone of lingual artery and the middle of greater horn of hyoid bone were also measured. RESULT: The pathway of the lingual artery had high variation, but the relative position between the segment of the greater horn of hyoid bone of lingual artery and the greater horn of hyoid bone were relatively constant. The lingual artery run forward approximately parallel to the greater horn of hyoid bone into tongue (2.32 +/- 1.29) mm in the superior to the greater horn of hyoid bone or (2.00 +/- 1.68) mm in the inferior to the greater horn of hyoid bone The distances from the origin of the lingual artery to the bifurcation of the common carotid artery and tip of the greater horn of hyoid bone were (12.93 +/- 7.36) mm and (10.40 +/- 5.75) mm separately. CONCLUSION The lingual artery could be clearly shown in CTA and the anatomy data in physiological condition of the lingual artery could be obtained by CTA. There was important instructive significance for clinical treatment concerning about the lingual artery.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Angiography ; Arteries ; anatomy & histology ; Female ; Humans ; Male ; Middle Aged ; Tomography, Spiral Computed ; Tongue ; anatomy & histology ; blood supply ; Young Adult

Objective:To explore the influencing factors of HIV-1 DNA and its clinical value.Methods:The relationship between HIV-1 DNA and CD4 + T cell count, CD4/CD8, HIV viral load and subtype was analyzed in 304 patients with HIV/AIDS in order to explore the factors affecting HIV-1 DNA and the value of clinical application. Results:There was no statistically significant difference in HIV-1 DNA levels between the different CD4 + T cell level groups (Z=1.194, P>0.05). HIV-1 DNA levels were higher in the CD4/CD8≤0.5 group than in the CD4/CD8>0.5 group (Z=-2.788, P<0.01). HIV-1 DNA levels were higher in the HIV viral load >100 copies/ml group than in the ≤100 copies/ml group (Z=-2.953, P<0.01). HIV-1 DNA levels were higher in those with CD4 + T cell counts ≤200 at diagnosis than in those with CD4 + T cell counts >200 at diagnosis (Z=-2.175, P<0.05). HIV-1 DNA levels were higher in patients with CD4/CD8 ≤0.2 at diagnosis than in those with CD4/CD8 between 0.2 and 0.5, but there was no significant difference between the two groups (Z=-0.893, P>0.05). HIV-1 DNA levels were higher in both groups than in the CD4/CD8≥0.5 group (Z=-2.568, Z=-1.960, P<0.05). Higher HIV-1 DNA levels were found in people with an HIV viral load >100, 000 copies/ml at diagnosis than in people with an HIV viral load ≤100, 000 copies/ml at diagnosis (Z=-3.520, P<0.001). The level of HIV-1 DNA was higher in the CRF01_AE group than in the non-CRF01_AE group, and the difference was statistically significant (Z=-2.848, P<0.01). CD4/CD8 seemed to be a protective factor for HIV-1 DNA>500 copies/ml. (OR=0.214(95%CI: 0.056~0.822, P<0.05) Conclusion:CD4 + T lymphocyte count, CD4/CD8, viral load and subtype are factors that influence HIV-1 DNA levels, while HIV-1 DNA may be informative for immune status assessment and disease progression determination.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.