Comparison of the effect of ultrasounic-harmonic scalpel and electrocautery in the treatment of axillary lymph nodes during radical surgery for breast cancer.

OBJECTIVE: To compare the efficacy of ultrasounic-harmonic scalpel and electrocautery in the treatment of axillary lymph nodes during radical surgery for breast cancer. METHODS: A prospective study was conducted in the Department of Breast Surgery, Zhongda Hospital Affiliated to Southeast University. A total of 128 patients with pathologically confirmed breast cancer who were treated by the same surgeon from July 2023 to November 2023 were included in the analysis. All breast operations were performed using electrocautery, and surgical instruments for axillary lymph nodes were divided into ultrasounic-harmonic scalpel group and electrocautery group using a random number table. According to the extent of lymph node surgery, it was divided into four groups: sentinel lymph node biopsy, lymph node at station I, lymph node at station I and II, and lymph node dissection at station I, II and III. Under the premise of controlling variables such as BMI, age and neoadjuvant chemotherapy, the effects of ultrasounic-harmonic scalpel and electrocautery in axillary surgery were compared. RESULTS: Compared with the electrosurgical group, there were no significant differences in lymph node operation time, intraoperative blood loss, postoperative axillary drainage volume, axillary drainage tube indwelling time, postoperative pain score on the day after surgery, and the incidence of postoperative complications (p>0.05). CONCLUSION: There is no significant difference between ultrasounic-harmonic scalpel and electrocautery in axillary lymph node treatment for breast cancer patients, which can provide a basis for the selection of surgical energy instruments.

Differences of serum glucose and lipid metabolism and immune parameters and blood metabolomics regarding the transition cows in the antepartum and postpartum period.

This study aims to investigate differences in metabolism regarding the transition cows. Eight cows were selected for the test. Serum was collected on antepartum days 14th (ap14) and 7th (ap7) and postpartum days 1st (pp1), 7th (pp7), and 14th (pp14) to detect biochemical parameters. The experiment screened out differential metabolites in the antepartum (ap) and postpartum (pp) periods and combined with metabolic pathway analysis to study the relationship and role between metabolites and metabolic abnormalities. Results: (1) The glucose (Glu) levels in ap7 were significantly higher than the other groups (p < 0.01). The insulin (Ins) levels of ap7 were significantly higher than pp7 (p = 0.028) and pp14 (p < 0.01), and pp1 was also significantly higher than pp14 (p = 0.016). The insulin resistance (HOMA-IR) levels of ap7 were significantly higher than ap14, pp7, and pp14 (p < 0.01). The cholestenone (CHO) levels of ap14 and pp14 were significantly higher than pp1 (p < 0.01). The CHO levels of pp14 were significantly higher than pp7 (p < 0.01). The high density lipoprotein cholesterol (DHDL) levels of pp1 were significantly lower than ap14 (p = 0.04), pp7 (p < 0.01), and pp14 (p < 0.01), and pp14 was also significantly higher than ap14 and ap7 (p < 0.01). (2) The interferon-gamma (IFN-γ) and tumor necrosis factor α (TNF-α) levels of ap7 were significantly higher than pp1 and pp7 (p < 0.01); the immunoglobulin A (IgA) levels of pp1 were significantly higher than ap7 and pp7 (p < 0.01); the interleukin-4 (IL-4) levels of pp7 were significantly higher than ap7 and pp1 (p < 0.01), the interleukin-6 (IL-6) levels of ap7 and pp1 were significantly higher than pp7 (p < 0.01). (3) Metabolomics identified differential metabolites mainly involved in metabolic pathways, such as tryptophan metabolism, alpha-linolenic acid metabolism, tyrosine metabolism, and lysine degradation. The main relevant metabolism was concentrated in lipid and lipid-like molecules, organic heterocyclic compounds, organic acids, and their derivatives. The results displayed the metabolic changes in the transition period, which laid a foundation for further exploring the mechanism of metabolic abnormalities in dairy cows in the transition period.

Classification of FLT3 inhibitors and SAR analysis by machine learning methods.

FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anti-cancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS fingerprints, ECFP4 fingerprints, and TT fingerprints were used to represent the inhibitors in the dataset. A total of 36 classification models were built based on support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural networks (DNN) algorithms. Model 3D_3 built by deep neural networks (DNN) and TT fingerprints performed best on the test set with the highest prediction accuracy of 85.83% and Matthews correlation coefficient (MCC) of 0.72 and also performed well on the external test set. In addition, we clustered 3867 inhibitors into 11 subsets by the K-Means algorithm to figure out the structural characteristics of the reported FLT3 inhibitors. Finally, we analyzed the SAR of FLT3 inhibitors by RF algorithm based on ECFP4 fingerprints. The results showed that 2-aminopyrimidine, 1-ethylpiperidine,2,4-bis(methylamino)pyrimidine, amino-aromatic heterocycle, [(2E)-but-2-enyl]dimethylamine, but-2-enyl, and alkynyl were typical fragments among highly active inhibitors. Besides, three scaffolds in Subset_A (Subset 4), Subset_B, and Subset_C showed a significant relationship to inhibition activity targeting FLT3.

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a training set and a test set using two splitting methods: (1) Kohonen's self-organizing map and (2) random splitting. The molecular structures were represented by MACCS fingerprints, RDKit fingerprints, topological torsions fingerprints and ECFP4 fingerprints. A total of 80 classification models were built by using five machine learning methods, including decision tree (DT), random forest, support vector machine, eXtreme Gradient Boosting and deep neural network. Model 15A_2 built by the XGBoost algorithm based on ECFP4 fingerprints showed the best performance, with an accuracy of 88.08% and an MCC value of 0.76 on the test set. Finally, we clustered the 7313 HDAC1 inhibitors into 31 subsets, and the substructural features in each subset were investigated. Moreover, using DT algorithm we analyzed the structure-activity relationship of HDAC1 inhibitors. It may conclude that some substructures have a significant effect on high activity, such as N-(2-amino-phenyl)-benzamide, benzimidazole, AR-42 analogues, hydroxamic acid with a middle chain alkyl and 4-aryl imidazole with a midchain of alkyl whose α carbon is chiral.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

Effects of maize straw treated with various levels of CaO and moisture on composition, structure, and digestion by in vitro gas production.

OBJECTIVE: The objective of this study was to explore the effects of maize straw treated with calcium oxide (CaO) and various moisture, on the composition and molecular structure of the fiber, and gas production by fermentation in an in vitro rumen environment. METHODS: The experiment used 4×3 Factorial treatment. Maize straws were treated with 4 concentrations of CaO (0%, 3%, 5%, and 7% of dry straw weight) and 3 moisture contents (40%, 50%, and 60%). Scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray fluorescence spectroscopy were employed to measure the surface texture, secondary molecular structure of carbohydrate, and calcium (Ca) content of the maize straw, respectively. The correlation of secondary molecular structures and fiber components of maize straw were analyzed by CORR procedure of SAS 9.2. In vitro rumen fermentation was performed for 6, 12, 24, 48, and 72 h to measure gas production. RESULTS: Overall, the moisture factor had no obvious effect on the experimental results. Neutral detergent fiber (NDF), acid detergent fiber, acid detergent lignin, hemicellulose and cellulose contents decreased (p<0.05) with increasing concentrations of CaO treatment. Surface and secondary molecular structure of maize straw were affected by various CaO and moisture treatments. NDF had positive correlation (p<0.01) with Cell-H (H, height), Cell-A (A, area), CHO-2-H. Hemicellulose had positive correlation (p<0.01) with Lignin-H, Lignin-A, Cell-H, Cell-A. Ca content of maize straw increased as the concentration of CaO was increased (p<0.01). Gas production was highest in the group treated with 7% CaO. CONCLUSION: CaO can adhere to the surface of the maize straw, and then improve the digestibility of the maize straw in ruminants by modifying the structure of lignocellulose and facilitating the maize straw for microbial degradation.

Quantifying urban wastewater treatment sector's greenhouse gas emissions using a hybrid life cycle analysis method - An application on Shenzhen city in China.

Substantial amounts of greenhouse gas (GHG) emissions generated at urban wastewater treatment plants (WWTP) are gaining increasing appreciation. Improving upon the commonly used Process-Based Life-Cycle Analysis (PLCA) and Environmentally-Extended Life-Cycle Analysis (EIO-LCA) models, we construct a Hybrid Life Cycle Analysis (HLCA) model and quantify both direct and indirect GHG emissions at the operational stage of WWTPs in Shenzhen, one of the fastest urbanizing cities in the world. Data are collected from 26 wastewater treatment plants in Shenzhen, out of all 32, covering 5 commonly used wastewater treatment technologies in China, i.e. Sequencing Batch Reactor, Oxidation Ditch, Biological Filter, AAO-MBR and AAO. The results show that WWTPs using AAO-MBR technology have the highest GHG emission intensity, averaging 0.79 tons per m3, primarily due to its large electricity intensity required. WWTPs using other technologies emit 0.27 to 0.39 tons of GHGs per m3 of wastewater treated. GHG emissions associated with electricity use occupy the largest share, ranging from 65 to 75%. Therefore, transforming the energy structure of the electric power sector to low-carbon sources can reduce WWTPs operational GHG emissions. In total, GHG emissions from Shenzhen's urban wastewater sector have increased from below 0.5 million tons in 2012 to over 0.6 million tons in 2017. Inter-model comparison shows that EIO-LCA substantially underestimates the urban wastewater sector's GHG emissions using the water sector's average parameters while PLCA also results in minor underestimations due the omission of indirect emissions in the production stage of chemicals and other material inputs.

Expression and Function of WNT6: From Development to Disease.

WNT family member 6 (WNT6) is a member of the highly conserved WNT protein family. It plays an essential role in the normal development process, not only in embryonic morphogenesis, but also in post-natal homeostasis. WNT6 functions in mice and humans. This review summarizes the current findings on the biological functions of WNT6, describing its involvement in regulating embryogenesis, decidualization, and organ development. Aberrant WNT6 signaling is related to various pathologies, such as promoting cancer development, lung tuberculosis, and kidney fibrosis and improving the symptoms of Rett syndrome (RTT). Thus, due to its various functions, WNT6 has great potential for in-depth research. This work not only describes the signaling mechanism and function of WNT6 under physiological and pathological conditions, but also provides a theoretical basis for targeted therapy.

Optimization of Ionic Liquid-Assisted Extraction of Biflavonoids from Selaginella doederleinii and Evaluation of Its Antioxidant and Antitumor Activity.

As new green solvents, ionic liquids (ILs) have been generally applied in the extraction and separation of natural product. In this study, microwave assisted extraction based on IL (IL-MAE) was firstly employed to extract total biflavonoids from Selaginella doederleinii. Based on single-factor experiment, microwave power (300-700 W), extract time (30-50 min) and extract temperature (40-60 °C) on total bioflavonoids and antioxidant activities of the extracts were further investigated by a Box-Behnken design of response surface methodology (RSM) selecting total bioflavonoids yields and IC50 of radical scavenging as index. Besides antioxidant activity of the extract was evaluated by a 2,2-diphenyl-1-picrylhydarzyl (DPPH) and 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulphonate (ABTS) radical scavenging assay, ferric reducing power assay and chelation of ferrous ions assay, and then anticaner activity was also researched against A549 cell line and 7721 cell line. The results illustrated that three factors and their interactions could be well suited for second-order polynomial models (p < 0.05). Through process parameters, optimization of the extract (460 W, 40 min, and 45 °C) and detection of bioactivity, the yield of total bioflavonoids was 16.83 mg/g and IC50 value was 56.24 µg/mL, respectively, indicating the extract has better anti-oxidation effect and antitumor activity. Furthermore, IL-MAE was the most efficient extracting method compared with MAE and Soxhlet extraction, which could improve extraction efficiency in a shorter time and at a lower temperature. In general, ILs-MAE was first adopted to establish a novel and green extraction process on the yields of total biflavonoids from S. doederleinii. In addition, the extract of containing biflavones showed potent antioxidant and anticancer capacity as a utilized valuable bioactive source for natural medicine.