Osteosarcoma(OS)is one of the most common malignant tumors in bone tissue,and its specific mechanism is not yet fully clear. Studies have shown that OS cells express different cytokines(CKs)and their receptors in the development stage of tumors,and enable them to grow autonomously and confer metastatic ability through autocrine and paracrine effects. In this regard,the most important CKs mainly includes insulin-like growth factor-1,transforming growth factor-β,chemokine 5 and interleukin-8,which can regulate the tumor microenvironment that is conducive to tumor growth,invasion and metastasis. This review summarizes the role and mode of action of CKs and their biological relevance to OS cells,hoping to provide effective new markers and therapeutic targets for clinical treatment of OS.

Objective To explore the impact and protective mechanisms of head cooling on neural activity during passive hyperthermia.Methods Sixteen subjects were randomly exposed for 1 h to three different conditions: normal (25℃), hot (50℃) and head cooling (chamber:50℃,cold packs:5℃),after environment exposure, rs-fMRI were performed.Regional homogeneity(ReHo) datum at three different conditions were analyzed by REST2.0 to obtain brain areas with statistical difference.Brain voxel with statistical difference were selected as ROIs to ReHo values and were analyzed by One-Way ANOVA with SPSS18.0.Neural activity of brain areas with statistical difference were compared in any two groups by Post hoc.Results The brain regions showing differences among three groups included right orbital frontal cortex,left middle frontal gyrus,bilateral amygdala,left middle temporal gyrus,left hippocampus,bilateral parietal inferior, left precentral gyrus.Compared with normal group, ReHo increased in right orbital frontal cortex, and decreased in left precentral gyrus,left middle frontal gyrus,left parietal inferior,but no changed in bilateral amygdala,left middle temporal gyrus,left hippocampus,right parietal inferior in head cooling group.Compared with hot group,head cooling group showed increased ReHo in left middle temporal gyrus,left hippocampus,right parietal inferior,and decreased ReHo in bilateral amygdala,left parietal inferior,unchanged ReHo in right orbital frontal cortex, left precentral gyrus, left middle frontal gyrus.Conclusion The specified alterations of ReHo may reflect that the head cooling could partially eliminate the impact of passive hyperthermia, and is closely linked with emotional function.

【Objective】 To compare the short-term clinical effects of oblique lateral interbody fusion （OLIF） and transforaminal lumbar interbody fusion （TLIF） for treating single-segment lumbar spondylolisthesis. 【Methods】 We retrospectively analyzed the data of 68 patients with single-segment degenerative lumbar spondylolisthesis from January 2019 to February 2020. According to different surgical methods， the patients were divided into OLIF+ anterior screw fixation group （33 cases） and TLIF + posterior pedicle screw fixation group （35 cases）. The operation time， intraoperative blood loss， postoperative drainage， postoperative hospital stay and complication rate were compared between the two groups of patients. The disc height （DH）， lumbar lordosis （LL）， fused segmental lordosis （FSL）， foraminal height （FH）， and spondylolisthesis angle （SA） were measured before and after surgery and during follow-up. The visual analogue scale （VAS） of waist pain and the Oswestry disability index （ODI） were used to evaluate the short-term clinical efficacy. 【Results】 The operation time， intraoperative blood loss， postoperative drainage， and postoperative hospital stay were less in OLIF group than in TLIF group （all P<0.05）. There was no statistically significant difference in VAS or ODI scores between the two groups at the last follow-up after surgery （both P>0.05）. The two groups had statistically significant differences in DH and FH after surgery （P<0.05）， but no significant difference in postoperative LL， FSL or SA （all P>0.05）. There were six （18.2%） and five （14.3%） cases of complications in OLIF group and TLIF group， respectively， with no significant difference （P>0.05）. 【Conclusion】 OLIF and TLIF are equally safe and effective in treating single-segment lumbar spondylolisthesis. However， OLIF combined with anterior screw fixation has the advantages of less surgical trauma， less blood loss， shorter operation time， reduced postoperative hospital stay and shorter recovery time. Therefore， it is a more minimally invasive surgical option.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.