Hasil untuk "hep-ex"

P. Shyamsundar

The paper titled"An implementation of neural simulation-based inference for parameter estimation in ATLAS"by the ATLAS collaboration (arXiv:2412.01600v1 [hep-ex]) describes the implementation of neural simulation-based inference for a measurement analysis performed by ATLAS. The uncertainties in the analysis arising from the finiteness of the simulated datasets are estimated using a novel double-bootstrapping technique described in that work. In the present comment, it is claimed and demonstrated, using a toy example, that the double-bootstrapping technique does not actually capture the aforementioned uncertainties.

Amy L. Collins, Keara Kirkness, E. Ramon‐Gil et al.

Background: Disease modeling is vital for our understanding of disease mechanisms and for developing new therapeutic strategies. Accurately modeling the intact tumor microenvironment (TME) is increasingly recognized as essential for gaining insights into cancer biology and therapeutic response. Preclinical mouse models have provided utility for studying the evolving TME, but these models are costly and can lead to animal suffering and the discontinuation of drug investigations. To address these limitations, particularly in hepatocellular carcinoma (HCC), we have developed an ex vivo model using tumor precision-cut slices (TPCS) derived from orthotopic liver tumors. Methods: Murine HCC tumors were generated via intrahepatic injection of Hep-53.4 cells, providing a source of tumor tissue for TPCS generation. Subsequent scaling to a 96-well format and modification to include a secreted luciferase enabled longitudinal ex vivo screening of 26 drugs applied at 2 doses over an 8-day period, using just 5 tumors. One drug identified in the screen, salinomycin, was then validated in vivo via intraperitoneal injection of mice with orthotopic liver tumors. Results: Histological characterization determined that TPCS maintain the architecture, cellular complexity, and drug responsiveness of the original HCC-TME under simplified culture conditions that preserve viability and metabolic activity. In addition to typical HCC therapies, sorafenib and anti-PD1 immunotherapy, the screen identified 2 drugs as potent anticancer agents capable of impacting the viability of TPCS: salinomycin and rottlerin. Salinomycin was further validated in vivo, significantly reducing tumor burden without evidence of toxicity. Conclusions: We present a 3Rs (Reduction, Refinement, Replacement) approach for studying HCC biology and performing 96-well-scale drug screening within an intact, metabolically active TME, offering a more ethical and effective platform for drug discovery.

Patricia Santos Barbosa, G. E. Souza, S. E. C. Maluf et al.

We present insights into the mechanism of action of marinoquinolines (MQ), a novel class of lead candidates. Using a divergent synthetic approach, we developed a series of 20 new analogues with fluorescence properties. Structure–activity relationships analysis identified 19 as an attractive compound showing a combination of favorable in vitro (IC50 3D7 = 0.28 μM; CC50 HepG2 = 53 μM), ex vivo (EC50 Pf = 1.2 μM; EC50 Pv = 0.53 μM), in vivo (3 × 50 mg/kg oral dose resulted in a 96% reduction in parasitemia in Plasmodium berghei-infected mice), physicochemical (Sol7.4 = 171 μM; LogD7.4 = 3.9), and pharmacokinetic (P_app = 9.4 × 10–6 cm/s, human Clint hep,mic = 0.61–0.68 μL min–1 mg–1) properties. Compound 19 selectively accumulates in infected erythrocytes, enters the digest vacuole and inhibits Plasmodium falciparum proteolytic activity, suggesting that MQs act as protease inhibitors. These findings strengthen the evidence that MQs are promising lead candidates for antimalarial drug discovery.

K. Noda, N. Atale, Taylor J. Austin et al.

Background: as we look to extend ex vivo lung perfusion times (EVLP) to improve preservation, the metabolic activity of the lungs will require support from other organ functions. Active functional liver support, including detoxification, synthesis, and regulation, can improve lung preservation during EVLP. This study aimed to demonstrate the effects of hepatic conditioning of the EVLP perfusate on lung endothelium, via the receptor of advanced glycation end-products (RAGE)-nuclear-factor-κB (NF-κB) signaling in vitro. Methods: we performed in vitro experiments using human lung microvascular endothelial cells (HLMVECs), human hepatocytes, and perfusate (Steen solution). Four experimental groups: 1) fresh Steen (negative controls, NC), 2) EVLP’ed Steen control, this solution collected after 12 h of EVLP of human lungs, 3) hepatocyte conditioned EVLP’ed Steen (Hep-cond.), and 4) a RAGE inhibitor added in EVLP’ed Steen (RAGE inhibitor). HLMVECs were incubated in each testing condition and exposed to hypoxia (1% O2/8% CO2) for 24 h. Media were collected to investigate NF-κB signaling and endothelial glycocalyx damage. Results: HLMVECs incubated under hypoxia in EVLP’ed Steen showed significantly upregulated NF-κB signal and endothelial damage denoted by increased glycosaminoglycans and matrix metalloproteinase-2 activity among the groups. The Hep-cond. solution significantly attenuated those findings, while the RAGE inhibitor attenuated the NF-κB signal but not endothelial glycocalyx damage. Conclusion: Our study demonstrates that hepatic function incorporated into EVLP can ameliorate pulmonary endothelial cells injury under hypoxic normothermic perfusion exposure. Our data supports the concept of incorporating other organ functions into an organ perfusion platform, to enhance lung graft preservation.

C. Cottle, M. Anbazhagan, A. Lipat et al.

Crohn’s disease (CD) is characterized by chronic inflammation of the mucosa, which involves the release of cytokines and chemokines that promotes further activation and infiltration of leukocytes. 1 Leukocyte trafficking to the gut is mediated by the interaction of chemokines with G-protein-coupled receptors, and hence, this interaction can be therapeutically targeted to control mucosal inflammation. 2 Despite this therapeutic potential, clinical trials have yet to show efficacy in chemokine-blocking intervention for CD management. 3 For example, CCL25 recruits CCR9-expressing leukocytes, and blocking this interaction in a phase III clinical trial with Vercirnon was shown to be ineffective in the treatment of moderate to severe CD. 4 This suggests the involvement of more than one chemokine that needs to be targeted in CD management, and the secretory chemokines of intestinal epithelium are unknown. In a recent report, we established an experimental protocol for defining the epithelial secretome in conditioned media of intestinal organoids derived from mucosal biopsies of a pediatric population and showed several interleukins, growth factors, and cytokines released from these cells. 5 In the present study, we have extended those findings using the previous technique on non-inflammatory bowel disease (IBD) and CD pediatric patient-derived ileal organoids (IOs) to answer the following questions: (1) What are the different chemokines produced by human ileal epithelium in the absence of in vivo factors? (2) Does ex vivo chemokine secretion from the intestinal epithelium differ in composition

Yuexiang Li, L. Shen

Human epithelial type 2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies in autoimmune diseases. As the HEp-2 cell has hundreds of different patterns, none of currently available HEp-2 datasets contain all of the types. Therefore, existing automatic processing systems for HEp-2 cells, e.g., cell segmentation and classification, needs to be transferred between different data sets. However, the performances of transferred system often dramatically decrease, especially when transferring supervised-approaches, e.g., deep learning network, from large dataset to the small but similar ones. In this paper, a novel transfer-learning framework using generative adversarial networks (cC-GAN) is proposed for robust segmentation of different HEp-2 datasets. The proposed cC-GAN tries to solve the overfitting problem of most deep learning networks and improves their transfer-capacity. An improved U-net, so-called Residual U-net (RU-net), is developed to work as the generator for cC-GAN model. The cC-GAN was first trained and tested using I3A dataset and then directly evaluated using MIVIA dataset, which is much smaller than I3A. The segmentation result demonstrates the excellent transferring-capacity of our cC-GAN framework, i.e., a new state-of-the-art segmentation accuracy of 75.27% was achieved on MIVIA without finetuning.