The anti-non-small cell lung cancer effect of Diosbulbin B: Targeting YY1 induced cell cycle arrest and apoptosis.

BACKGROUND: Toxic components frequently exhibit unique characteristics and activities, offering ample opportunities for the advancement of anti-cancer medications. As the main hepatotoxic component of Dioscorea bulbifera L. (DB), Diosbulbin B (DIOB) has been widely studied for its anti-tumor activity at nontoxic doses. However, the effectiveness and mechanism of DIOB against non-small cell lung cancer (NSCLC) remains unclear. PURPOSE: To evaluate the anti-NSCLC activity of DIOB and to elucidate the specific mechanism of action. METHOD: The effect of DIOB on NSCLCL in vitro was evaluated through CCK8, colony formation, and flow cytometry. The in vivo efficacy and safety of DIOB in treating NSCLC were assessed using various techniques, including HE staining, tunel staining, immunohistochemistry, and biochemical index detection. To understand the underlying mechanism, cell transfection, western blotting, molecular docking, cellular thermal shift assay (CESTA), and surface plasmon resonance (SPR) were employed for investigation. RESULTS: DIOB effectively hindered the progression of NSCLC both in vitro and in vivo settings at a no-observed-adverse-effect concentration (NOAEC) and a safe dosage. Specifically, DIOB induced significant G0/G1 phase arrest and apoptosis in A549, PC-9, and H1299 cells, while also notably inhibiting the growth of subcutaneous tumors in nude mice. Mechanistically, DIOB could directly interact with oncogene Yin Yang 1 (YY1) and inhibit its expression. The reduction in YY1 resulted in the triggering of the tumor suppressor P53, which induced cell cycle arrest and apoptosis in NSCLC cells by inhibiting the expression of Cyclin A2, B2, CDK1, CDK2, CDK4, BCL-2, and inducing the expression of BAX. In NSCLC cells, the induction of G0/G1 phase arrest and apoptosis by DIOB was effectively reversed when YY1 was overexpressed or P53 was knocked down. Importantly, we observed that DIOB exerted the same effect by directly influencing the expression of YY1-regulated c-Myc and BIM, particularly in the absence of P53. CONCLUSION: For the inaugural investigation, this research unveiled the anti-NSCLC impact of DIOB, alongside its fundamental mechanism. DIOB has demonstrated potential as a treatment agent for NSCLC due to its impressive efficacy in countering NSCLC.

Response surface methodology optimization of extraction and enrichment conditions of total triterpenoid saponins from Celosiae Semen and evaluation of its lipid-lowering activity.

The saponin-enriched extract from Celosiae Semen is a promising resource owing to its lipid-lowering activity. However, triterpenoid saponins are difficult to extract owing to their high molecular weight and strong water solubility. The aim of this paper was to explore an eco-friendly and effective technology of extraction and enrichment of total triterpenoid saponins to obtain high lipid-lowering fractions. Initially, Box-Behnken design experiments were employed to optimize the heat reflux extraction process on the basic of mono-factor experiments. Afterwards, the crude extract was further purified using D-101 resin, and the purification parameters were investigated based on adsorption/desorption experiments and biological activity assay. Under optimal conditions, the purity of the finally obtained total triterpenoid saponins was increased by 7.28-fold. The lipid-lowering activities of the six main triterpenoid saponins were evaluated in HepG2 cells induced by palmitic acid. The results of Oil Red O staining showed that the compounds all exhibited potential lipid-lowering activity. The structure-activity relationship analysis suggested that the oligosaccharide chain at C-28 played an essential role in their lipid-lowering activity and the substituent group at C-23 site also showed important effects. The optimal extraction and purification methods may facilitate the utilization of Celosiae Semen for the industrial production as a functional food and drug.

[Automatic Segmentation of Digital Pathology Slides Based on Unsupervised Learning].

OBJECTIVE: To segment images through an unsupervised method as an alternative to manual labeling. METHODS: A total of 100 whole slide image (WSI) data of HE stained and Pap stained slides were selected as the research and test objects, including 70 breast slides, 20 lung slides and 10 thyroid slides. In order to ensure the diversity of data, the breast slides included those of normal tissue, inflammation and tumor, the lung slides were mainly neoplasms in the lower lobe, including those of inflammation and tumor, and the thyroid slides were of cells, all benign, obtained through fine needle aspiration. The maximum total magnification (original magnification) of each image was 400 times, and the file format was NDPI. Each WSI was manually labeled, and the labeled area of each WSI was more than 10 fields of vision. The labeled information was to be used for validity verification. An unsupervised image segmentation technique based on superpixel and fully convolution neural network algorithms was constructed and used to segment any region of interest (ROI) of unlabeled WSI. In comparison with the region adjacency graph merging method, the segmentation effect of the two methods was assessed with the under segmentation error, the boundary recall and the mean Intersection-over-Union, and the efficiency of the two methods was also compared. In the comparison of execution efficiency, the test process included the preprocessing time of superpixel, and excluded the time of loading the deep learning engine. RESULTS: Unsupervised automatic segmentation was implemented for any ROI region of WSI according to the texture and color. The results of the breast slides, lung slides and thyroid slides showed slight differences, and multiple tests yielded stable results. However, the performance of this method in differentiating inflammation and tumor was average. The under-segmentation error, the boundary recall and the mean Intersection-over-Union were 19.10%, 82.06% and 45.06%, respectively. The under segmentation error, the boundary recall and the mean Intersection-over-Union for the region adjacency graph merging method were 21.52%, 78.39% and 44.81%, respectively. The average time consumption of the whole process was 0.27 s in GPU mode and 1.30 s in CPU mode. The average time consumption of the region adjacency graph merging method was 10.5 s in CPU mode because the method of region adjacency graph merging was not realized in the GPU mode. CONCLUSION: This method produced ideal pixel level labeling results through simple human-computer interaction, which could effectively reduce the cost of digital pathology slide data labeling. Compared with the region adjacency graph merging method, this method had better performance in processing image texture and had faster processing speed.

[Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer].

OBJECTIVE: To study the different methods of artificial intelligence (AI)-assisted Ki-67 scoring of clinical invasive ductal carcinoma (IDC) of the breast and to compare the results. METHODS: A total of 100 diagnosed IDC cases were collected, including slides of HE staining and immunohistochemical Ki-67 staining and diagnosis results. The slides were scanned and turned into whole slide image (WSI), which were then scored with AI. There were two AI scoring methods. One was fully automatic counting by AI, which used the scoring system of Ki-67 automatic diagnosis to do counting with the whole image of WSI. The second method was semi-automatic AI counting, which required manual selection of areas for counting, and then relied on an intelligent microscope to conduct automatic counting. The diagnostic results of pathologists were taken as the results of pure manual counting. Then the Ki-67 scores obtained by manual counting, semi-automatic AI counting and automatic AI counting were pairwise compared. The Ki-67 scores obtained from the manual counting (pathological diagnosis results), semi-automatic AI and automatic AI counts were pair-wise compared and classified according to three levels of difference: difference ≤10%, difference of >10%-<30% and difference ≥30%. Intra-class correlation coefficient ( ICC) was used to evaluate the correlation. RESULTS: The automatic AI counting of Ki-67 takes 5-8 minutes per case, the semi-automatic AI counting takes 2-3 minutes per case, and the manual counting takes 1-3 minutes per case. When results of the two AI counting methods were compared, the difference in Ki-67 scores was all within 10% (100% of the total), and the ICC index being 0.992. The difference between manual counting and semi-automatic AI was less than 10% in 60 cases (60% of the total), between 10% and 30% in 37 cases (37% of the total), and more than 30% in only 3 cases (3% of the total), ICC index being 0.724. When comparing automatic AI with manual counting, 78 cases (78% of the total) had a difference of ≤10%, 17 cases (17% of the total) had a difference of between 10% and 30%, and 5 cases (5%) had a difference of ≥30%, the ICC index being 0.720. The ICC values showed that there was little difference between the results of the two AI counting methods, indicating good repeatability, but the repeatability between AI counting and manual counting was not particularly ideal. CONCLUSION: AI automatic counting has the advantage of requiring less manpower, for the pathologist is involved only for the verification of the diagnosis results at the end. However, the semi-automatic method is better suited to the diagnostic habits of pathologists and has a shorter turn-over time compared with that of the fully automatic AI counting method. Furthermore, in spite of its higher repeatability, AI counting, cannot serve as a full substitute for pathologists, but should instead be viewed as a powerful auxiliary tool.

Pharmacological characterization of JWX-A0108 as a novel type I positive allosteric modulator of α7 nAChR that can reverse acoustic gating deficits in a mouse prepulse inhibition model.

The α7 nicotinic acetylcholine receptor (α7 nAChR) is a ligand-gated Ca2+-permeable homopentameric ion channel implicated in cognition and neuropsychiatric disorders. Pharmacological enhancement of α7 nAChR function has been suggested for improvement of cognitive deficits. In the present study, we characterized a thiazolyl heterocyclic derivative, 6-(2-chloro-6-methylphenyl)-2-((3-fluoro-4-methylphenyl)amino)thiazolo[4,5-d]pyrimidin-7(6H)-one (JWX-A0108), as a novel type I α7 nAChR positive allosteric modulator (PAM), and evaluated its ability to reverse auditory gating and spatial working memory deficits in mice. In Xenopus oocytes expressing human nAChR channels, application of JWX-A0108 selectively enhanced α7 nAChR-mediated inward current in the presence of the agonist ACh (EC50 value = 4.35 ± 0.12 µM). In hippocampal slices, co-application of ACh and JWX-A0108 (10 µM for each) markedly increased both the frequency and amplitude of spontaneous inhibitory postsynaptic currents (sIPSCs) recorded in pyramidal neurons, but JWX-A0108 did not affect GABA-induced current in oocytes expressing human GABAA receptor α1ß3γ2 and α5ß3γ2 subtypes. In mice with MK-801-induced deficits in auditory gating, administration of JWX-A0108 (1, 3, and 10 mg/kg, i.p.) dose-dependently attenuates MK-801-induced auditory gating deficits in five prepulse intensities (72, 76, 80, 84, and 88 dB). Furthermore, administration of JWX-A0108 (0.03, 0.1, or 0.3 mg/kg, i.p.) significantly reversed MK-801-induced impaired spatial working memory in mice. Our results demonstrate that JWX-A0108 is a novel type I PAM of α7 nAChR, which may be beneficial for improvement of cognitive deficits commonly found in neuropsychiatric disorders such as schizophrenia and Alzheimer's disease.