Construction of single domain camel antibody library against breast cancer cellular antigens

زمینه و هدف: آنتی بادی های زنجیره سنگین شتری Camelied-derived heavy chain) (VHH= یک آنتی بادی شتری بوده و کوچک ترین واحد باند شونده به آنتی ژن است. اندازه کوچک نانوبادی ها بزرگ ترین مزیت آن ها می باشد که سبب دستکاری ژنتیکی راحت آن ها می شود. این مطالعه با هدف ساخت کتابخانه ی آنتی بادی تک دمین از شتر ایمن شده با یک رده سلولی آدنوکارسینومای سینه ی انسان (SKBR3) طراحی و اجرا شد. روش بررسی: در این مطالعه تجربی ابتدا عصاره سلول SKBR3 طی سه نوبت به صورت زیر پوستی به یک شتر تزریق گردید. سپس RNA کامل از طحال شتر استخراج و قطعات VHH به کمک روش RT-PCR ساخته و تکثیر شدند. قطعات VHH در درون فاژمید Pcomb3x قرار گرفتند و به روش الکتروپوریشن قطعات نوترکیب وارد باکتری های DH5α شدند. تنوع کتابخانه ی تهیه شده توسط تکنیک انگشت نگاری آنزیمی مورد بررسی قرار گرفت و در نهایت بیان VHH با روش SDS-PAGE ارزیابی شد. یافته ها: در این پژوهش کتابخانه آنتی بادی شتری با بیش از 105 کلونی ساخته شد. همچنین انگشت نگاری آنزیمی نشان داد که کتابخانه ی آنتی بادی حاصل دارای تنوع بالایی می باشد. بررسی های اولیه بوسیله SDS-PAGE مشخص کرد که پروتئین VHH با وزن مولکولی 15 کیلو دالتون در باکتری های ترانسفورم شده بیان می شود. نتیجه گیری: تهیه ی کتابخانه ی آنتی بادی ایمن بر ضد رده سلولی SKBR3، امکان جداسازی آنتی بادی های اختصاصی VHH بر ضد آنتی ژن های مختلف سرطان سینه را فراهم می کند

Prediction of Bubble Point Pressure & Asphaltene Onset Pressure During CO2 Injection Using ANN & ANFIS Models

Although CO2 injection is one of the most common methods in enhanced oil recovery, it could alter fluid properties of oil and cause some problems such as asphaltene precipitation. The maximum amount of asphaltene precipitation occurs near the fluid pressure and concentration saturation. According to the description of asphaltene deposition onset, the bubble point pressure has a very special importance in optimization of the miscible CO2 injection. The purpose of this research is to predict the onset of asphaltene and bubble point pressure of fluid reservoir using artificial intelligence developed models including a software simulator called “Intelligent Proxy Simulator (IPS)” based on structure artificial neural networks and “adaptive neural fuzzy inference system”, which is a combination of fuzzy logic and neural networks. To evaluate the predictions by artificial intelligence networks at the onset of deposition, a solid model using Winprop software was employed. Standing correlations were used for comparison of bubble point pressure. The results obtained using artificial intelligence models in prediction of the onset of asphaltene deposition and bubble point pressure during injection of CO2 were more accurate than those obtained from the thermodynamics Solid model and the Standing correlation respectively

Synthesis and characterization of Sm2(MoO4)3, Sm2(MoO4)3/GO and Sm2(MoO4)3/C3N4 nanostructures for improved photocatalytic performance and their anti-cancer the MCF-7 cells

Samarium molybdate nanoparticles (Sm2(MoO4)3) were prepared through a hydrothermal procedure and were used to form various composites with graphene oxide (GO) and carbon nitride (C3N4). The changes in the dimensions and morphology of the products were prepared using template agents like cetyltrimethyl ammonium bromide (CTAB), Sodium dodecyl sulfate (SDS) (�90), Triton X-100 (90), Polyvinyl alcohol (95), Ethylene glycol (�99), and polyvinylpyrrolidone (PVP). DRS analysis indicated band gap for the Sm2(MoO4), Sm2(MoO4)3/GO, and Sm2(MoO4)3/C3N4 as 3.75, 3.15, and 3.4 respectively. The characteristics of the prepared nanostructures were studied through X-ray diffraction (XRD), energy dispersive X-ray (EDX), and scanning electron microscopy (SEM). Finally, the activity of the prepared Sm2(MoO4)3 as photo-catalysts for the degradation of different organic dyes such as methyl orange (MO), methylene blue (MB), and rhodamine B (Rh B) was evaluated. The photocatalytic property of Sm2(MoO4)3/C3N4 and Sm2(MoO4)3/GO for the degradation of MO, was obtained. Based on the empirical data Sm2(MoO4)3/C3N4 had the strongest photodegradation effect as compared to the other compounds tested after around 40 min. BET analysis revealed that the specific surface area of the Sm2(MoO4)3 nanocomposite prepared using C3N4 is 15 times that of in the absence of C3N4. Also, the cytotoxicity of synthesized samples was evaluated using MTT assay against human cell lines MCF-7 (cancer), and its IC50 was about 125 mg/L. © 202

Predicting future climate scenarios: a machine learning perspective on greenhouse gas emissions in agrifood systems

Global climate change is an extensive phenomenon characterized by alterations in weather patterns, temperature trends, and precipitation levels. These variations substantially impact agrifood systems, encompassing the interconnected components of farming, food production, and distribution. This article analyzes 8,100 data points with 27 input features that quantify diverse aspects of the agrifood system’s contribution to predicted Greenhouse Gas Emissions (GHGE). The study uses two machine learning algorithms, Long-Short Term Memory (LSTM) and Random Forest (RF), as well as a hybrid approach (LSTM-RF). The LSTM-RF model integrates the strengths of LSTM and RF. LSTMs are adept at capturing long-term dependencies in sequential data through memory cells, addressing the vanishing gradient problem. Meanwhile, with its ensemble learning approach, RF improves overall model performance and generalization by combining multiple weak learners. Additionally, RF provides insights into the importance of features, helping to understand the significant contributors to the model’s predictions. The results demonstrate that the LSTM-RF algorithm outperforms other algorithms (for the test subset, RMSE = 2.977 and R2 = 0.9990). These findings highlight the superior accuracy of the LSTM-RF algorithm compared to the individual LSTM and RF algorithms, with the RF algorithm being less accurate in comparison. As determined by Pearson correlation analysis, key variables such as on-farm energy use, pesticide manufacturing, and land use factors significantly influence GHGE outputs. Furthermore, this study uses a heat map to visually represent the correlation coefficient between the input variables and GHGE, enhancing our understanding of the complex interactions within the agrifood system. Understanding the intricate connection between climate change and agrifood systems is crucial for developing practices addressing food security and environmental challenges

Synthesis of MIL-101(Cr)/Sulfasalazine (Cr-TA@SSZ) hybrid and its use as a novel adsorbent for adsorptive removal of organic pollutants from wastewaters

Abstract Metal-organic frameworks (MOFs) are considered strong adsorbents to the removal of organic pollutants due to their unique characteristics. In this work, a new type of metal-organic porous material MIL-101(Cr)/Sulfasalazine (Cr-TA@SSZ) hybrid successfully synthesized by a hydrothermal approach for the first time. The synthesized Cr-TA@SSZ and MIL-101(Cr) adsorbents were applied for adsorption of terephthalic acid (TA), para-toluic acid (p-tol), and benzoic acid (BA). The Cr-TA@SSZ and MIL-101(Cr) were characterized by the general tests including X-ray diffraction (XRD), Fourier transform infrared (FTIR), Brunauer-Emmett-Teller (BET), transmission electron microscopy (TEM), Scanning electron microscope(SEM), thermal gravimetric analysis (TGA), differential thermal analysis (DTA), zeta potential, and Elemental analysis (EDX). Based on the above analyses, it was concluded that Cr-TA@SSZ has a different composition and network structure to the MIL-101(Cr). The formula for new MOF (Cr-TA@SSZ) proposed as: Cr3F (H2O)2O[C6H4(CO2)2][C6H3N(OH)(CO2)], 2.5H2. The experiments for evaluating the effect of the different parameters such as pH, initial concentration, contact time, and temperature on the removal of the terephthalic acid (TA), para-toluic acid (p-tol), and benzoic acid (BA) were carried out in batch mode. The isotherm, kinetic and thermodynamic models were also analyzed for the adsorption of TA, p-tol, and BA. Equilibrium adsorption was evaluated employing Langmuir, Freundlich, Temkin, and Redlich–Peterson equations, in which Langmuir and Redlich–Peterson models were in good agreement with the experimental results. Maximum adsorption capacity (q0) of the Cr-TA@SSZ for terephthalic acid (TA), para-toluic acid (p-tol), and benzoic acid (BA) were obtained 2208.4 mg.g− 1, 1241.2 mg.g− 1, and 1009.5 mg.g− 1, respectively while for MIL-101(Cr) were obtained 1692.0 mg.g− 1, 952.4 mg.g− 1, and 769.2 mg.g− 1 respectively. The Cr-TA@SSZ was found to be more efficient in the removal of terephthalic acid (TA), para-toluic acid (p-tol), and benzoic acid (BA) from water than the MIL-101(Cr). Also, the results showed that a pseudo-second-order kinetic model with a higher correlation coefficient (R2 &gt; 0.99) matched well for the adsorption of terephthalic acid (TA), para-toluic acid (p-tol), and benzoic acid (BA) onto MIL-101(Cr) and Cr-TA@SSZ. The thermodynamic parameters such as a change in Gibbs free energy (ΔG), enthalpy (∆H), and entropy (ΔS) were determined and the negative values of ΔG indicated that the process of removal was spontaneous at all values of temperatures. Further, the values of ∆H indicated the exothermic nature of the removal process. Moreover, adsorption experiments using industrial wastewater from a TA production plant showed that Cr-TA@SSZ‌ can be used as a promising adsorbent in the adsorptive removal of organic pollutants from wastewaters. This MOF was able to remove 40% COD from the concentrated phase (equivalent to 13000 ppm) and remove 77.3% COD from the diluted phase (equivalent to 4250 ppm) wastewater.</jats:p

Evaluation of phase equilibrium conditions of clathrate hydrates in natural gas binary mixtures: Machine learning approach

Hydrate formation temperature (T) is an important parameter for any industrial process that deals with natural gas hydrates. In this study, the Group Method of Data Handling (GMDH) approach is used to predict hydrate formation T in natural gas binary mixtures. A comprehensive database containing 728 data samples is compiled from 46 published experimental works. To find the best combination of input variables, different sets of input variables were assessed. A total of seven models were developed using different sets of input variables. Compared to the correlations proposed in the literature, the developed models in this study performed better and the model developed based on input Set #7 was the most accurate: RMSE values of 1.6381 and 1.5499 for the training and testing datasets, respectively. All models also were evaluated using a blind dataset-that was not included in testing or training-to check model applicability to wider data. Similarly, all GMDH models performed excellently for the external dataset, where the developed model based on input Set #5 showed the best performance: RMSE values of 1.3482. The findings of this study contribute to our understanding of hydrate formation conditions in natural gas binary mixtures. As pure and binary mixtures of natural gas main constituents are studied, the results can be especially useful for purification and energy transport applications.Reza Behvandi, Afshin Tatar, Amin Shokrollahi, Abbas Zeinijahrom