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Drilling fluid performs a crucial function in petroleum drilling operations. This functional fluid improves the performance of the drilling process by controlling bottom hole pressure, wellbore stability, and cleaning the hole. Circulation fluid loss frequently occurs during drilling, leading to several costly problems, such as formation damage and wellbore instability. This study investigates the potential of utilizing orange peel waste as an environmentally friendly and cheap additive to enhance the characteristics of the bentonite drilling fluid. Laboratory experiments were conducted to evaluate the impact of orange peel powder addition into the filtration fluid of drilling fluid with various concentrations (0, 0.1, 0.15, 0.2, 0.25, and 0.5 % by weight). Following American Petroleum Institute (API) guidelines, their rheological and filtration characteristics were measured at room conditions. The results stated that adding orange peel powder significantly improves the fluid loss control ability. By increasing the orange peel powder amount, the API filtration volume decreased up to 18.18 % as compared to the bentonite drilling fluid without orange peel powder addition.

Histone deacetylases (HDACs) represent a particularly intriguing focus in the discovery and advancement of anticancer drugs. In this study, we detail the design, synthesis, and evaluation of novel hydroxamic acids featuring a 1H-benzo[d]imidazole component as potent inhibitors of HDACs and potential anticancer agents. A total of 54 novel compounds were designed and synthesized. Biological assessment revealed that compounds 29c, 31c, and 33c were the most effective inhibitors of HDACs and demonstrated significant potential as anti-cancer agents. Furthermore, compound 31c exhibited a 3-fold higher inhibitory activity against the mixture of HDAC isoforms in HeLa cell nuclear extract compared to the reference compound SAHA. Finally, docking studies on HDAC2 and HDAC6 revealed hydrogen bonds and pi-stacking interactions between hydrophobic and hydrophilic residues on the binding pocket surface, particularly with benzimidazole rings and anilide substituents, which enhance the inhibitory potency of the studied compounds. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms have gained widespread application in constructing computational models for predicting the bioactivity and physicochemical properties of numerous compounds, notably HDAC inhibitors. In this work, 2801 unique compounds with confirmed bioassays on HDAC2 were collected and employed to train ML models to virtually screening and possibly design potential inhibitors for HDAC2. A meticulous 3‐step procedure was first proposed to ensure the hits are within the application domain of the screening models. Through virtual screening of 91 million compounds, 59 structures were suggested by the four highest predictability models as potential HDAC2 inhibitors. The bioactivity of these compounds is further confirmed through a validated docking protocol, wherein 57 out of the 59 active hits proposed by the four models exhibited superior affinity with HDAC2 compared to vorinostat, with docking scores ranging from −10.74 to −7.06 kcal/mol. Lead optimization strategies were then implemented on the top‐performing compound from the molecular docking scores, CID 122648337, enhancing its binding affinity and interactions within the HDAC2 active site. This optimization led to the creation of two novel lead compounds, demonstrating higher affinity to HDAC2 than the initial hits. The stability of lead compounds in the active site was confirmed via MD simulations. [ABSTRACT FROM AUTHOR]

Background and Aim: Autotaxin (ATX) is an extracellular lysophospholipase D that catalyzes the hydrolysis of lysophosphatidylcholine into lysophosphatidic acid (LPA). Recent accumulating evidence indicates the biological roles of ATX in malignant tumors. However, the expression and clinical implications of ATX in human cholangiocarcinoma (CCA) remain elusive. Methods: In this study, the expression of ATX in 97 human CCA tissues was evaluated by immunohistochemistry. Serum ATX levels were determined in CCA patients (n = 26) and healthy subjects (n = 8). Autotaxin expression in cell types within the tumor microenvironment was characterized by immunofluorescence staining. Results: High ATX expression in CCA tissue was significantly associated with a higher frequency of lymph node metastasis (p = 0.050). High ATX expression was correlated with shorter overall survival (p = 0.032) and recurrence‐free survival (RFS) (p = 0.001) than low ATX expression. In multivariate Cox analysis, high ATX expression (p = 0.019) was an independent factor for shorter RFS. Compared with low ATX expression, high ATX expression was significantly associated with higher Ki‐67‐positive cell counts (p < 0.001). Serum ATX levels were significantly higher in male CCA patients than in healthy male subjects (p = 0.030). In the tumor microenvironment of CCA, ATX protein was predominantly expressed in tumor cells, cancer‐associated fibroblasts, plasma cells, and biliary epithelial cells. Conclusions: Our study highlights the clinical evidence and independent prognostic value of ATX in human CCA. [ABSTRACT FROM AUTHOR]

Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32°) and bitumen (API 7.39°). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values.

Backgrounds: The success of direct‐acting antiviral (DAA) therapy provides a cure for patients chronically infected with hepatitis C virus (HCV); however, outcomes after hepatectomy for HCV‐associated hepatocellular carcinoma (HCC) before and after DAA introduction remain poorly studied. Methods: Patients who underwent R0/R1 hepatectomy for HCV‐associated HCC were retrospectively analyzed. Two time periods were defined: Pre‐DAA (2007–2011, December 2013 was defined as the end of follow‐up) and Post‐DAA groups (2014–2018, December 2020 was defined as the end of follow‐up). Propensity score matching (PSM) analyses were performed to highlight the effect of DAA therapy. Results: A total of 155 patients with HCV‐associated HCC were included in this study (Pre‐DAA group, n = 103 and post‐DAA group, n = 52). In the Post‐DAA group, DAA therapy was performed in 26 patients (50.0%), and all of these patients achieved sustained virologic response (SVR) (preoperative SVR, n = 7; postoperative SVR, n = 19). There was no significant difference between the two groups regarding surgical settings and tumor pathology. There was no significant difference in the 5‐year overall survival (OS) rate (61.1% and 64.8%, pre‐ and post‐DAA group, respectively, p = 0.441); meanwhile, the 5‐year recurrence‐free survival (RFS) rate in the post‐DAA group was better than the pre‐DAA group (21.1% and 40.2%, p = 0.073) with a trend toward significance. After PSM except for the postoperative SVR status, there were no significant differences in OS (p = 0.586) and RFS (p = 0.888). Conclusions: This study showed that survival outcomes were not changed in hepatectomized cases of HCV‐associated HCC before and after the introduction of DAA therapy. [ABSTRACT FROM AUTHOR]