Objective To identify and verify proteins that interact and collaborate with ATF3 in inhibiting hepatocarcinogenesis.Methods Immunoprecipitation (IP),co-IP and protein spectrum analysis were used to identify the protein which interacted with ATF3 in HepG2.Immunohistochemistry (IHC) and Western blot (WB) were used to detect the expression pattern of ATF3 and its candidate interacting proteins in liver tissue.Results The protein expression differences were detected by IP in two HepG2 groups.The experimental group was infected by lentiviral vector with ATF3 over-expression and the control group was infected by mock-vehicle.Several protein bands with expression diversity were analyzed by protein spectrum,which revealed several candidate proteins that may be related with ATF3.Peptide sequences were analyzed by Mascot software and NCBI database.Combined with the existing literature and our study results,Gelsolin (GSN) was identified as a protein closely interacting with ATF3 and confirmed by co-IP,IHC and WB.Conclusions GSN is identified and verified as an interacting protein with ATF3.ATF3 may function as a suppressor of liver cancer via protein-protein interactions with Gelsolin.

Objective To observe the long-term clinical effect of pars plana vitrectomy combined with fovea-sparing internal limiting peeling in the treatment of macular foveoschisis in pathologic myopic. Methods A prospective case series study. Fifteen patients (15 eyes) with pathological myopic macular foveoschisis who received treatment in Eye Hospital of Wenzhou Medical University from December 2015 to December 2016 were enrolled. There were 4 males (4 eyes) and 11 females (11eyes), with an average age of 55.33±8.34 years. All patients underwent BCVA, diopter, spectral domain OCT and axial length measurement. The mean logMAR BCVA was 0.95±0.64. The mean central fovea thickness (CFT) was 576.00±185.32 μm. All patients underwent vitrectomy combined with fovea-sparing internal limiting peeling. After gas-liquid exchange, 12％ C3F8 was filled and followed up at 1, 3, 6 and 12 months after surgery. Follow-up time was more than 12 months. The structural changes of BCVA and macular area were observed.Results The foveal internal limiting membranes was successfully preserved in all eyes using the techinique. At the final follow-up, the CFT was 258.60±175.22 μm and the BCVA was 0.46±0.43, which were significantly improved compared with preoperative measurements (t=4.90, 5.20;P<0.001). Macular foveoschisis was resovled in 13 eyes. BCVA increased in 14 eyes. Internal limiting membranes proliferation and contraction occurred in 5 eyes and full-thickness macular hole occurred in 1 eye.Conclusions Pars plana vitrectomy with fovea-sparing internal limiting peeling is effective in the treatment of myopic macular retinoschisis. It can improve BCVA and CFT.

Objective: To explore the differences in the gut microbiota of primary school students with different levels of sugar sweetened beverage intake, so as to provide scientific evidence for better identification of health risks in children and the development of targeted health policies. Methods: In June 2022, a total of 192 healthy primary school students from Chengdu were selected using a stratified cluster random sampling method. The sugar sweetened beverage intake was assessed through a dietary frequency questionnaire. Based on the median daily sugar sweetened beverage intake, primary school students were categorized into a low intake group ( n =96) and a high intake group ( n =96). The gut microbiota in fresh fecal samples from the two groups of primary school students was analyzed using 16S rRNA high throughput sequencing, and the diversity and community structure differences in the gut microbiota were compared. Results: Children in the low intake group had a sugar sweetened beverage intake of (21.3±1.6) mL/d, while the high intake group had an intake of (269.6±37.3) mL/d. Diversity analysis results showed that there were no statistically significant differences between the low intake and the high intake group in terms of α diversity metrics: Observed_otus index [298.50 (259.75, 342.25), 305.50 (244.25, 367.75)], Goods_coverage index [1.00 (1.00, 1.00), 1.00 (1.00, 1.00)], Chao index [304.18 (260.75, 348.78), 305.88 (245.68, 370.88)], Shannon index [5.88 (5.29, 6.45), 5.71 (4.89, 6.28)] and Simpson index [0.95 (0.91, 0.97), 0.94 (0.88, 0.97)] ( Z =-0.64, -0.76, -0.54, -1.76, -1.67, P >0.05). Furthermore, no statistically significant difference was observed in β diversity between the two groups ( R 2=0.006, P >0.05). At the genus level, the abundance of Blautia [0.033 (0.018, 0.055)] and Fusicatenibacter [0.009 (0.005, 0.015)] were higher in the low intake group compared to the high intake group [0.024 (0.013, 0.041),0.006 (0.003, 0.011)]and differences were statistically significant ( Z =-2.52, -2.81, P <0.05). LEfSe analysis highlighted intergroup differences primarily in Blautia, Fusicatenibacter and Sarcina( LDA= 3.56,3.12,3.53, P <0.05). Conclusions There is no significant difference in the diversity and overall structure of the gut microbiota in primary school students with different levels of sugar sweetened beverage intake. However, there are species variations at the genus level. The information can serve as a scientific basis for identifying health risks in primary school students and formulating targeted health strategies.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.