The chemical constituents from the n-butanol fraction of ethanol extract of safflower (Carthamus tinctorius L.) were isolated and purified using chromatographic process including MCI Gel CHP-20, ODS, Sephadex LH-20 column chromatography, and semi-preparative HPLC method, and one alkyne and two phenylpropanoid derivants were obtained. Their structures were identified as (5R,E)-tetradecane-12-ene-8,10-diyne-1,5,14-triol (1), (E)-8-O-β-D-glucopyranosyl n-butyl cinnamate (2), and (7S,8S)-7-(4-hydroxy-3-methoxybenzene)-7-butyl-8,9-diol (3) by modern spectroscopy methods (1D NMR, 2D NMR, UV, IR and MS). 1-3 are all new compounds. Compounds 1-3 were screened for their anti-renal fibrosis activities in vitro, and none of them showed obvious effect.

The AP2/ERF transcription factor family is a class of transcription factors widely present in plants, playing a crucial role in regulating flowering, flower development, flower opening, and flower senescence. Based on transcriptome data from flower, leaf, and stem samples of two Lonicera macranthoides varieties, 117 L. macranthoides AP2/ERF family members were identified, including 14 AP2 subfamily members, 61 ERF subfamily members, 40 DREB subfamily members, and 2 RAV subfamily members. Bioinformatics and differential gene expression analyses were performed using NCBI, ExPASy, SOMPA, and other platforms, and the expression patterns of L. macranthoides AP2/ERF transcription factors were validated via qRT-PCR. The results indicated that the 117 LmAP2/ERF members exhibited both similarities and variations in protein physicochemical properties, AP2 domains, family evolution, and protein functions. Differential gene expression analysis revealed that AP2/ERF transcription factors were primarily differentially expressed in the flowers of the two L. macranthoides varieties, with the differentially expressed genes mainly belonging to the ERF and DREB subfamilies. Further analysis identified three AP2 subfamily genes and two ERF subfamily genes as potential regulators of flower development, two ERF subfamily genes involved in flower opening, and two ERF subfamily genes along with one DREB subfamily gene involved in flower senescence. Based on family evolution and expression analyses, it is speculated that AP2/ERF transcription factors can regulate flower development, opening, and senescence in L. macranthoides, with ERF subfamily genes potentially serving as key regulators of flowering duration. These findings provide a theoretical foundation for further research into the specific functions of the AP2/ERF transcription factor family in L. macranthoides and offer important theoretical insights into the molecular mechanisms underlying floral phenotypic differences among its varieties.
Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Transcription Factors/chemistry* ; Lonicera/classification* ; Flowers/metabolism* ; Phylogeny ; Gene Expression Profiling ; Multigene Family

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

Jasmonic acid (JA) is a common plant hormone with regulatory effects on plant growth and development. The jasmonate ZIM-domain (JAZ) proteins (JAZs), as key regulators in the JA signaling pathway, are involved in multiple biological processes such as anthocyanin accumulation, flowering time modulation, and secondary metabolite synthesis in plants. JAZs are essential components of many regulatory signaling networks. The JAZ genes, members of the plant-specific TIFY family, have been identified in the genomes of a variety of horticultural plants. Here, we summarized the research progress in the roles of JAZs in horticultural plants, aiming to give insights into the further study of the biological functions and regulatory networks of JAZ genes in plants.
Horticulture ; Repressor Proteins/metabolism* ; Plant Proteins/metabolism* ; Cyclopentanes/metabolism* ; Oxylipins/metabolism* ; Plants/metabolism* ; Plant Development

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 examine the impact of varied surgical treatment strategies on the prognosis of patients with initial resectable gastric cancer liver metastases (IR-GCLM).Methods:This is a retrospective cohort study. Employing a retrospective cohort design, the study selected clinicopathological data from the national multi-center retrospective cohort study database, focusing on 282 patients with IR-GCLM who underwent surgical intervention between January 2010 and December 2019. There were 231 males and 51 males, aging ( M(IQR)) 61 (14) years (range: 27 to 80 years). These patients were stratified into radical and palliative treatment groups based on treatment decisions. Survival curves were generated using the Kaplan-Meier method and distinctions in survival rates were assessed using the Log-rank test. The Cox risk regression model evaluated HR for various factors, controlling for confounders through multivariate analysis to comprehensively evaluate the influence of surgery on the prognosis of IR-GCLM patients. A restricted cubic spline Cox proportional hazard model assessed and delineated intricate associations between measured variables and prognosis. At the same time, the X-tile served as an auxiliary tool to identify critical thresholds in the survival analysis for IR-GCLM patients. Subgroup analysis was then conducted to identify potential beneficiary populations in different surgical treatments. Results:(1) The radical group comprised 118 patients, all undergoing R0 resection or local physical therapy of primary and metastatic lesions. The palliative group comprised 164 patients, with 52 cases undergoing palliative resections for gastric primary tumors and liver metastases, 56 cases undergoing radical resections for gastric primary tumors only, 45 cases undergoing palliative resections for gastric primary tumors, and 11 cases receiving palliative treatments for liver metastases. A statistically significant distinction was observed between the groups regarding the site and the number of liver metastases (both P<0.05). (2) The median overall survival (OS) of the 282 patients was 22.7 months (95% CI: 17.8 to 27.6 months), with 1-year and 3-year OS rates were 65.4% and 35.6%, respectively. The 1-year OS rates for patients in the radical surgical group and palliative surgical group were 68.3% and 63.1%, while the corresponding 3-year OS rates were 42.2% and 29.9%, respectively. A comparison of OS between the two groups showed no statistically significant difference ( P=0.254). Further analysis indicated that patients undergoing palliative gastric cancer resection alone had a significantly worse prognosis compared to other surgical options ( HR=1.98, 95% CI: 1.21 to 3.24, P=0.006). (3) The size of the primary gastric tumor significantly influenced the patients′ prognosis ( HR=2.01, 95% CI: 1.45 to 2.79, P<0.01), with HR showing a progressively increasing trend as tumor size increased. (4) Subgroup analysis indicates that radical treatment may be more effective compared to palliative treatment in the following specific cases: well/moderately differentiated tumors ( HR=2.84, 95% CI 1.49 to 5.41, P=0.001), and patients with liver metastases located in the left lobe of the liver ( HR=2.06, 95% CI 1.19 to 3.57, P=0.010). Conclusions:In patients with IR-GCLM, radical surgery did not produce a significant improvement in the overall prognosis compared to palliative surgery. However, within specific patient subgroups (well/moderately differentiated tumors, and patients with liver metastases located in the left lobe of the liver), radical treatment can significantly improve prognosis compared to palliative approaches.

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 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.

China is a major country for gastric cancer incidence, with the majority being at the advanced stage. Currently, there has been a profound change in the perioperative diagnosis and treatment mode of locally advanced gastric cancer. It has become a gradually recommended diagnostic and treatment process in both eastern and western countries gastric cancer guidelines for patients to undergo radical surgery for gastric cancer after neoadjuvant therapy, followed by postoperative adjuvant therapy. Neoadjuvant therapy can downregulate the tumor grade and clinical stage of gastric cancer, facilitating the successful conduct of radical surgery for gastric cancer. Moreover, it allows for further therapeutic selection based on treatment response and empirically guides adjuvant therapy. However, after neoadjuvant therapy, tumor tissue and the whole body of the patient will inevitably undergo a series of changes, which will affect the implementation process of radical surgery for gastric cancer and postoperative management. Therefore, this article intends to discuss the indications and progress of neoadjuvant therapy for gastric cancer, the timing of radical surgery, technical points, postoperative complications, and other aspects, in order to provide reference and guidance for colleagues in the industry.