Aim To explore the effect of kaempferol-7- 0-neohesperidoside (K70N) against prostate cancer (PCa) and the underlying mechanism. Methods The effect of K70N on the proliferation of PCa cell lines PC3, DU145, C4-2 and LNCaP was detected using CCK8 assay. The effect of K70N on migration ability of DU145 cells was determined by wound healing assay. The targets of K70N and PCa were screened from SuperPred and other databases. The common targets both related to K70N and PCa were obtained from the Venny online platform, a protein-protein interaction network (PPI) was constructed by the String and Cyto- scape. Meanwhile, the GO and KEGG functional enrichment were analyzed by David database. Then, a "drug-target-disease-pathway" network model was constructed. Cell cycle of PCa cells treated with K70N was analyzed by flow cytometry. The expressions of cycle-associated proteins including Skp2, p27 and p21 protein were detected by Western blot. Molecular docking between Skp2 and K70N was conducted by Sybyl X2. 0. Results K70N significantly inhibited the proliferation and migration of PCa cells. A total number of 34 drug-disease intersection targets were screened. The String results showed that Skp2 and p27, among the common targets, were the key targets of K70N for PCa treatment. Furthermore, GO and KEGG functional en-richment indicated that the mechanism was mainly related to the cell cycle. Flow cytometry showed that K70N treatment induced cell cycle arrest at the S phase. Compared with the control group, the protein expression level of Skp2 was significantly down-regulated, while the protein expression levels of p27 and p21 were up-regulated. The network molecular docking indicated that the ligand K70N had a good binding ability with the receptor Skp2. Conclusions K70N could inhibit the proliferation and migration of PCa cells, block the cell cycle in the S phase, which may be related to the regulation of cell cycle through the Skp2- p27/p21 signaling pathway.

ObjectiveTo develop traditional Chinese medicine （TCM） formulae for the treatment of nonsevere coronavirus disease 2019 （COVID-19） and to explore its anti-inflammatory mechanism. MethodsThe dysregulated signaling pathways were determined in macrophages from bronchoalveolar lavage fluid of COVID-19 patients and in lung epithelial cells infected with SARS-CoV-2 in vitro based on transcriptome analysis. A total of 102 TCM formulae for the clinical treatment of nonsevere COVID-19 were collected through literature. The pathway-reversing rates of these formulae in macrophages and lung epithelial cells were evaluated based on signature signaling pathways， and the basic formula was determined in conjunction with TCM theory. The commonly used Chinese materia medica for nonsevere COVID-19 were summarized from the 102 TCM formulae as abovementioned. And together with the screening results from the Pharmacopoeia of the People's Republic of China， a “Chinese materia medica pool” was esta-blished for the development of TCM formulae for COVID-19. The regulatory effects of each herb on signaling pathways were obtained based on targeted transcriptome analysis. Oriented at reversing dysregulated signaling pathways of COVID-19， the calculation was carried out， and the artificial intelligent methods for compositing formulae， that are exhaustive method and parallel computing， were used to obtain candidate compound formulas. Finally， with reference to professional experience， an innovative formula for the treatment of nonsevere COVID-19 was developed. The ethanol extract of the formula was evaluated for its anti-inflammatory effects by detecting the mRNA expression of interleukin 1b （Il1b）， C-X-C motif chemokine ligand 2 （Cxcl2）， C-X-C motif chemokine ligand 10 （Cxcl10）， C-C motif chemokine ligand 2 （Ccl2）， nitric oxide synthase 2 （Nos2）， and prostaglandin-endoperoxide synthase 2 （Ptgs2） using reverse transcription-quantitative polymerase chain reaction （RT-qPCR） in RAW264.7 cells treated with lipopolysaccharide （LPS）. ResultsIn macrophages and lung epithelial cells， 34 dysregulated signaling pathways associated with COVID-19 were identified respectively. The effects of the 102 formulae for clinical treatment of nonsevere COVID-19 were evaluated based on the dysregulated signaling pathways and targeted transcriptome， and the result showed that Yinqiao Powder and Pingwei Powder （银翘散合平胃散， YQPWP） ranked first， reversing 91.18% of the dysregulated signaling pathways in macrophages and 100% of the dysregulated signaling pathways in lung epithelial cells. Additionally， YQPWP had the function of scattering wind and clearing heat， resolving toxins and removing dampness in accordance with the pathogenesis of wind-heat with dampness in COVID-19. It was selected as the basic formula， and was further modified and optimized to develop an innovative fomula Qiaobang Zhupi Yin （翘蒡术皮饮， QBZPY） based on expert experience and artificial intelligence in composing formulae. QBZPY can reverse all the dysregulated signaling pathways associated with COVID-19 in macrophages and lung epithelial cells， with the reversing rates of 100%. The chief medicinal of QBZPY， including Lianqiao （Fructus Forsythiae）， Xixiancao （Herba Siegesbeckiae） and Niubangzi （Fructus Arctii）， can down-regulate multiple signaling pathways related with virus infection， immune response， and epithelial damage. RT-qPCR results indicated that compared with the model group， the QBZPY group down-regulated the mRNA expression of Il1b， tumor necrosis factor （Tnf）， Cxcl2， Cxcl10， Ccl2， Nos2 and Ptgs2 induced by LPS in RAW264.7 cells （P＜0.05 or P＜0.01）. ConclusionBased on targeted transcriptome analysis， expert experience in TCM and artificial intelligence， QBZPY has been developed for the treatment of nonsevere COVID-19. The ethanol extract of QBZPY has been found to inhibit mRNA expression of several pro-inflammatory genes in a cellular inflammation model.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective To observe the therapeutic effect and mechanism of Guhuaisi Kangfu Pills on rats with steroid-induced osteonecrosis of the femoral head(SONFH).Methods Sixty rats were randomly divided into blank group,model group,Xianling Gubao Capsules group and Guhuaisi Kangfu Pills low-,medium-and high-dose groups,10 rats in each group.Except for the blank group,the SONFH model was established by lipopolysaccharide combined with Glucocorticoid induction method in all other groups of rats.At the end of the intervention,for the femoral head,blood vessel radiography was performed to observe the microvascular changes in the bone marrow,and hematoxylin-eosin(HE)staining and calculation of the empty bone trap rate,Micro-CT scanning analysis,and compression experiments were carried out,and the real-time quantitative polymerase chain reaction(RT-qPCR)was used to detect the gene expressions of phosphatidylinositol 3-kinase(PI3K),protein kinase B(Akt)1,vascular endothelial growth factor(VEGF)and platelet endothelial cell adhesion molecule 1(CD31)in whole blood.Results Compared with the blank group,the blood supply in the femoral head medullary cavity of the model group was poor,the empty bone lacuna rate was increased(P＜0.05),the bone mineral density and bone volume fraction were significantly decreased(P＜0.05),the maximum load and elastic modulus of the femoral head were decreased(P＜0.05),and the mRNA expression levels of Akt1,PI3K,VEGF and CD31 in whole blood were decreased(P＜0.05).Compared with the model group,the blood supply in the femoral head medullary cavity was relatively good,the empty bone lacuna rate was decreased(P＜0.05),the bone mineral density,bone volume fraction,trabecular number and trabecular thickness were significantly increased(P＜0.05),the trabecular separation was significantly decreased(P＜0.05),the maximum load and elastic modulus of the femoral head were increased(P＜0.05),and the mRNA expression levels of Akt1,PI3K,VEGF and CD31 in the whole blood were increased(P＜0.05)in the high-dose group of Guhuaisi Kangfu Pills and Xianling Gubao Capsules group.There was no significant difference in the above indexes between the high-dose group of Guhuaisi Kangfu Pills and the Xianling Gubao Capsules group(P＞0.05).Conclusion Guhuaisi Kangfu Pills improves SONFH in rats,and its mechanism is related to the promotion of VEGF/PI3K/Akt pathway gene expression,thereby promoting angiogenesis.

Objective To investigate the relationship between appendicular skeletal muscle composition(muscle mass and muscle index)and glycosylated hemoglobin A1c(HbA1c)level in type 2 diabetes mellitus(T2DM)patients.Methods A total of 459 adult T2DM patients hospitalized in the Department of Endocrinology,Tangdu Hospital of Air Force Medical University from April 2021 to June 2022 were selected as the research objects.Bioelectrical impedance analysis was used to evaluate the body composition of the patients.The patients were divided into two groups according to HbA1c level:standard glycation group(HbA1c≤7.0%,n=145)and unqualified glycation group(HbA1c>7.0%,n=314).The two groups'general data,biochemical indexes,muscle content,fat content and other body composition were analyzed.Spearman correlation analysis and multiple logistic regression analysis were used to analyze the relationship between body composition and glycemic control in type 2 diabetic patients.Results The appendicular skeletal muscle mass and its index(ASMI)of the patients in the standard glycation group were better than those in the unqualified glycation group(P<0.05).However,there were no significant differences in gender,history of antidiabetic drugs and body fat(body fat content,limb fat content and visceral fat content)between the two groups(P>0.05).Spearman correlation analysis showed that appendicular skeletal muscle mass and its index were negatively correlated with HbA1c(r=-0.158,P=0.001;r=-0.187,P<0.001).Logistic regression analysis showed that upper limb skeletal muscle mass(OR=3.570,95%CI 2.293-5.559)and lower limb skeletal muscle mass(OR=1.297,95%CI 1.146-1.468)were independent protective factors for achieving glycation standard in HbA1c group.Conclusions The skeletal muscle mass of limbs is a protective factor for reaching the standard of HbA1c.With the increase of skeletal muscle mass of limbs,the level of HbA1c gradually decreases.Among them,the increase of upper limb muscle mass has a stronger correlation with reaching the standard of HbA1c.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.

This study aimed to explore the anti-inflammatory material basis and molecular mechanism of Artemisia stolonifera based on the analysis of the chemical components in different extracted fractions of A. stolonifera and their antioxidant and anti-inflammatory effects in combination with network pharmacology and molecular docking. Thirty-two chemical components were identified from A. stolonifera by ultra-performance liquid chromatography coupled to tandem quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS). Among them, there were 7, 21 and 22 compounds in water, n-butanol and ethyl acetate fractions, respectively. The antio-xidant capacity of different extracted fractions was evaluated by measuring their scavenging ability against 1,1-diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6-trinitrophenyl) hydrazyl(DPPH) and 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulphonic acid)(ABTS) free radicals and total antioxidant capacity [ferric reducing antioxidant power(FRAP) assay]. The inflammatory model of RAW264.7 cells was induced by lipopolysaccharide(LPS), and the levels of nitrite oxide(NO), tumor necrosis factor-α(TNF-α), interleukin-6(IL-6) in the supernatant and the mRNA expression of related inflammatory factors in cells were used to evaluate the anti-inflammatory effects. The results revealed that ethyl acetate fraction of A. stolonifera was the optimal antioxidant and anti-inflammatory fraction. By network pharmacology, it was found that flavonoids such as rhamnazin, eupatilin, jaceosidin, luteolin and nepetin could act on key targets such as TNF, serine/threonine protein kinase 1(AKT1), tumor protein p53(TP53), caspase-3(CASP3) and epidermal growth factor receptor(EGFR), and regulate the phosphatidylinositol-3-kinase-protein kinase B(PI3K-AKT) and mitogen-activated protein kinase(MAPK) signaling pathways to exert the anti-inflammatory effects. Molecular docking further indicated excellent binding properties between the above core components and core targets. This study preliminarily clarified the anti-inflammatory material basis and mechanism of ethyl acetate fraction of A. stolonifera, providing a basis for the follow-up clinical application of A. stolonifera and drug development.
Antioxidants/chemistry* ; Molecular Docking Simulation ; Artemisia ; Network Pharmacology ; Phosphatidylinositol 3-Kinases ; Anti-Inflammatory Agents/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Interleukin-6

ObjectiveTo observe the changes in the expression and distribution of G protein-gated inwardly rectifying potassium channel subunit 2 （GIRK2） in the dorsal root ganglion （DRG） and spinal cord dorsal horn of rats with remifentanil-induced hyperalgesia. MethodsHyperalgesia was induced by intravenous infusion of remifentanil 4 μg/kg/min for 2 h in adult male SD rats. At 6th hour and on days 1， 3 and 5 following remifentanil treatment， we used immunofluorescence to examine the changes in the GIRK2 distribution and expression. Immunoblotting was used to detect GIRK2 expression of the total protein and membrane protein in DRG and spinal dorsal horn of rats. Behavioral testing was applied to evaluate the effect of intrathecal injection of GIRK2-specific agonist ML297 on thermal nociceptive threshold on day 1 after remifentanil infusion. Resultsmmunofluorescence results showed that GIRK2 was mainly co-localized with IB4-positive small neurons in DRG and nerve fibers in spinal dorsal horn. GIRK2 expression was significantly downregulated following remifentanil treatment. Immunoblotting results revealed that on day 1 following intravenous infusion of remifentanil， compared with those in the control group， GIRK2 expression levels of the total protein and membrane protein in DRG （0.47 ± 0.10 vs. 1.01 ± 0.17， P < 0.001； 0.47 ± 0.11 vs. 1.06 ± 0.12， P < 0.001） and spinal dorsal horn （0.52 ± 0.09 vs. 1.10 ± 0.08， P < 0.001； 0.54 ± 0.10 vs. 1.01 ± 0.13， P < 0.001） were all significantly decreased. The behavioral results showed that intrathecal ML297 effect on thermal withdrawal latency was significantly reduced following remifentanil treatment （P < 0.001）. ConclusionsRemifentanil might induce hyperalgesia via down-regulating GIRK2 expression in rat DRG and spinal cord dorsal horn.

Objective: To examine the value of intravascular ultrasound (IVUS) for excimer laser ablation (ELA) combined with drug-coated balloon (DCB) in treating lower limb arteriosclerotic obliterans (ASO). Methods: As a prospective case series study, patients who underwent ELA combined with DCB for lower limb ASO with the guidance of IVUS from September 2021 to March 2022 at Department of Vascular Surgery, Zhongshan Hospital, Fudan University were enrolled prospectively. Lesion characteristics, procedure-related outcomes and complications were collected. The therapy outcomes were compared with baseline data by paired t test. Results: There were 8 males and 2 females, aged (72.0±5.9) years (range: 61 to 81 years). Of all the 11 lesions, there were 8 lesions in superficial femoral artery and 3 in popliteal artery. The lesion length was (7.0±2.4) cm (range: 3.2 to 9.8 cm). There were 4 chronic totally occlusion and 7 severe stenosis. All patients underwent the operation successfully. The technical success rate was 10/11. Bailout stenting was performed in one lesion because of flow-limiting dissection. Four lesions were grade 3 to 4 in peripheral artery calcium score system, and 9 lesions with calcification arc≥180°. Larger diameter drug-coated balloons were selected in 5 lesions after measurement of intravascular ultrasound. The follow-up time was (6.0±1.9) months (range: 3 to 9 months). The ankle-brachial index of the patient was significantly improved immediately after surgery (0.97±0.13 vs. 0.48±0.18, t=-7.60, P<0.01) and at 3 months after surgery (0.95±0.12 vs. 0.48±0.18, t=-7.17, P<0.01). The 3-month primary patency rate was 11/11, the target lesion reintervention was 0 and ulcer healing rate was 3/4. Conclusion: IVUS assisted ELA in the treatment of lower limb artery lesions is safe and effective in early stage.
Female ; Male ; Humans ; Laser Therapy ; Lower Extremity ; Ultrasonography ; Femoral Artery ; Ultrasonography, Interventional