Objective To investigate the expression of peroxisome proliferator-activated rec eptor γ (PPARγ) and glucokinase (GK) induced by Wnt signaling pathway in mice NIT-1 β-cells,and to explore the interaction between PPARγ and Wnt signaling pathways.Methods Recombinant Wnt3a protein was applied to NIT-1 beta-cells to activate Wnt signaling pathway.The expression of PPARγ was determined by real-time PCR and Western blotting.The expression of GK was determined by real time PCR.Results Wnt3a rapidly activated Wnt/β-catenin/TCF signaling pathway,and increased PPARγ and GK mRNA expression by 41.2％ and 65.0％ in NIT-1cells,with PPARγ protein expression increasing by 97.8％ (P＜0.01).These effects were abrogated by Wnt and PIK3 inhibitors,dickkopf 1 and wortmannin treatment (P＜ 0.01).Conclusions PPARγ and GK can be upregulated by Wnt singnaling,and the effects might partially be PI3K-dependent.

Objective To analyse CT and DSA appearances of intrahepatic cholangiocarcinoma,and to improve understanding of intrahepatic cholangiocarcinoma.Methods Plain and enhanced CT scanning were performed in 17 patients with intrahepatic cholangiocarcinoma pathollogically proved,6 patients(6/17) performed dynamic CT scanning,5 performed DSA examinations.Results Pre-contrast CT scanning was a single low density lesions in all patients,post-contrast CT scanning showed slightly inhomogeneous enhancement in 17 patients,3 patients(3/17) showed low density unenhancement area with margin enhancement,2 patients delayed enhancement;Intrahepatic billary delatation was found in 15 patients and was within the lesion in 10 patients(10/15);On DSA,5 patients showed enlaragement increasment and rigidity of supplying arteries and tumour stain.Conclusion CT scanning and DSA were important,investigative methods for intrahepatic cholangiocarcinoma,the billary dilatations with slightly inhomogeneous enhancement lesions is important sign in diagnosising intrahepatic cholangiocarcinoma.

Objective To investigate risk factors for the development of advanced schistosomiasis. Methods The case\|control study was designed with a match of 1∶1 and 1∶2. Healthy persons and chronic schistosomiasis patients were used as control. Each group was composed of 213 cases. Items investigated included: history of schistosomiasis and treatment, exposure to the contaminated water, social\|economical condition and nutritional status, other concomitant diseases. Cellular and humoral immunity, HBVMs and ABO blood groups were also detected. Statistical analysis was performed by 1∶1 and 1∶2 matched single and stepwise conditional logistic regression analysis with SAS software. Results By stepwise conditional logistic regression analysis, it was revealed that number of schistosomiasis examination(OR=1\^168-1\^311), interval from first infection to last treatment(OR=1\^142), interval from first infection to this investigation (OR=1\^089), \{HBsAg\++\}(OR=4\^683-10\^759), \{HBcAb\++\} (OR=2\^873), \{HBsAg\++\}+\{HBeAb\++\}+\{HBcAb\++\}(OR=7\^64) were risk factors of developing advanced schistosomiasis. The average living space and cellular immunity were lower in advanced schistosomiasis patients than others. No association was found between advanced schistosomiasis and ABO blood groups. Conclusion The development of advanced schistosomiasis is associated with repeated infections, and delayed or incomplete treatment. Combined infection with hepatitis B, poor socio\|economic conditions and impaired cellular immunity may increase the pathogenetic risk of schistosomiasis.

Objective:To evaluate spectral CT metal artifacts reduction (MAR) technique in reducing metal artifacts of spinal implants in a phantom.Methods:Ovine spines were chosen as anthropomorphic phantom. The phantom including the pedicle screws, 3D-printed vertebral body (VB) and mesh cage were examined using spectral CT. Postoperative CT images were reconstructed at 70—140 keV with 10 keV interval of MAR and non-MAR. Artifact index (AI) and signal-to-noise ratio (SNR) were evaluated by CT and SD values in ROIs around the implants. Visibility of bony structures, the artifacts of pedicle screw, 3D-printed VB and mesh cage were subjectively evaluated. Plotting curves of AI and SNR with the increasing keV were drawn. The AI and SNR were compared at lower (70 keV), medium (100 keV) and high (130 keV) level between MAR and non-MAR images using the paired t-test, and the subjective scores were compared using Wilcoxon signed rank-sum test. Results:The AI values around pedicle screws (anterior, posterior and lateral), 3D-printed VB and mesh cage decreased with the increase of keV, while SNR improved in MAR and non-MAR images. The AI values in the anterior, lateral and posterior pedicle screws and lateral titanium implants were significantly lower in MAR than those in non-MAR ( P<0.05). The AI value in posterior 3D-printed vertebral was lower in MAR than that of non-MAR only at 70 keV ( P<0.001). The SNR values in the anterior and posterior pedicle screws, 3D-printed VB increased with the increase of keV, but decreased in other ROIs. In the subjective evaluation, the image scores of MAR were higher than those of non-MAR ( P<0.05). Conclusion:Spectral CT using the MAR reconstruction can effectively reduce metal artifacts of spinal implants. The effect is better in pedicle screw and mesh cage than 3D-printed VB.

The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years. The anatomy and disease symptoms of the spine are complex, and the diagnosis and treatment of spinal surgery require rich clinical experience. However, the distribution of medical resources in China is seriously uneven. How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved. Artificial intelligence is a technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. With the advent of the era of big data medical technology, artificial intelligence technology may solve this problem by transforming "experts sinking" into "tech sinking" . At present, technologies such as confrontation learning, weakly supervised learning, intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence, and have also played an important role in many fields of clinical medicine. Based on the advantages of deep learning and neural network in disease learning, many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis, low back pain, lumbar degenerative diseases, spinal deformity, spinal tumors, and other spine-related diseases. The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases. In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery, it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors, reduce the rate of misdiagnosis and missed diagnosis, and effectively reduce the economic and social burden of spinal diseases. This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad, and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.