Functionalized graphene oxide-based lamellar membranes for organic solvent nanofiltration applications.

In this study, two-dimensional graphene oxide-based novel membranes were fabricated by modifying the surface of graphene oxide nanosheets with six-armed poly(ethylene glycol) (PEG) at room conditions. The as-modified PEGylated graphene oxide (PGO) membranes with unique layered structures and large interlayer spacing (∼1.12 nm) were utilized for organic solvent nanofiltration applications. The as-prepared 350 nm-thick PGO membrane offers a superior separation (>99%) against evans blue, methylene blue and rhodamine B dyes along with high methanol permeance ∼ 155 ± 10 L m-2 h-1, which is 10-100 times high compared to pristine GO membranes. Additionally, these membranes are stable for up to 20 days in organic solvent. Hence the results suggested that the as-synthesized PGO membranes with superior separation efficiency for dye molecules in organic solvent can be used in future for organic solvent nanofiltration application.

Lamellar Graphene Oxide-Based Composite Membranes for Efficient Separation of Heavy Metal Ions and Desalination of Water.

Sufficient efforts have been carried out to fabricate highly efficient graphene oxide (GO) lamellar membranes for heavy metal ion separation and desalination of water. However, selectivity for small ions remains a major problem. Herein, GO was modified by using onion extractive (OE) and a bioactive phenolic compound, i.e., quercetin. The as-prepared modified materials were fabricated into membranes and used for separation of heavy metal ions and water desalination. The GO/onion extract (GO/OE) composite membrane with a thickness of 350 nm shows an excellent rejection efficiency for several heavy metal ions such as Cr6+ (∼87.5%), As3+ (∼89.5%), Cd2+ (∼93.0%), and Pb2+ (∼99.5%) and a good water permeance of ∼460 ± 20 L m-2 h-1 bar-1. In addition, a GO/quercetin (GO/Q) composite membrane is also fabricated from quercetin for comparative studies. Quercetin is an active ingredient of onion extractives (2.1% w/w). The GO/Q composite membranes show good rejection up to ∼78.0, ∼80.5, ∼88.0, and 95.2% for Cr6+, As3+, Cd2+, and Pb2+, respectively, with a DI water permeance of ∼150 ± 10 L m-2 h-1 bar-1. Further, both membranes are used for water desalination by measuring rejection of small ions such as NaCl, Na2SO4, MgCl2, and MgSO4. The resulting membranes show >70% rejection for small ions. In addition, both membranes are used for filtration of Indus River water and the GO/Q membrane shows remarkably high separation efficiency and makes river water suitable for drinking purpose. Furthermore, the GO/QE composite membrane is highly stable up to ∼25 days under acidic, basic, and neutral environments as compared to GO/Q composite and pristine GO-based membranes.

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening-based approaches.

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided.

Influence of Operating and Electrochemical Parameters on PEMFC Performance: A Simulation Study.

Proton exchange membrane fuel cell, or polymer electrolyte fuel cell, (PEMFC) has received a significant amount of attention for green energy applications due to its low carbon emission and less other toxic pollution capacity. Herein, we develop a three-dimensional (3D) computational fluid dynamic model. The values of temperature, pressure, relative humidity, exchange coefficient, reference current density (RCD), and porosity values of the gas diffusion layer (GDL) were taken from the published literature. The results demonstrate that the performance of the cell is improved by modifying temperature and operating pressure. Current density is shown to degrade with the rising temperature as explored in this study. The findings show that at 353 K, the current density decreases by 28% compared to that at 323 K. In contrast, studies have shown that totally humidified gas passing through the gas channel results in a 10% higher current density yield, and that an evaluation of a 19% higher RCD value results in a similar current density yield.

A comprehensive review of key factors affecting the efficacy of antibody drug conjugate.

Antibody Drug Conjugate (ADC) is an emerging technology to overcome the limitations of chemotherapy by selectively targeting the cancer cells. ADC binds with an antigen, specifically over expressed on the surface of cancer cells, results decrease in bystander effect and increase in therapeutic index. The potency of an ideal ADC is entirely depending on several physicochemical factors such as site of conjugation, molecular weight, linker length, Steric hinderance, half-life, conjugation method, binding energy and so on. Inspite of the fact that there is more than 100 of ADCs are in clinical trial only 14 ADCs are approved by FDA for clinical use. However, to design an ideal ADC is still challenging and there is much more to be done. Here in this review, we have discussed the key components along with their significant role or contribution towards the efficacy of an ADC. Moreover, we also explained about the recent advancement in the conjugation method. Additionally, we spotlit the mode of action of an ADC, recent challenges, and future perspective regarding ADC. The profound knowledge regarding key components and their properties will help in the synthesis or production of different engineered ADCs. Therefore, contributes to develop an ADC with low safety concern and high therapeutic index. We hope this review will improve the understanding and encourage the practicing of research in anticancer ADCs development.

Ultrathin Graphene Oxide-Based Nanocomposite Membranes for Water Purification.

Two-dimensional graphene oxide (GO)-based lamellar membranes have been widely developed for desalination, water purification, gas separation, and pervaporation. However, membranes with a well-organized multilayer structure and controlled pore size remain a challenge. Herein, an easy and efficient method is used to fabricate MoO2@GO and WO3@GO nanocomposite membranes with controlled structure and interlayer spacing. Such membranes show good separation for salt and heavy metal ions due to the intensive stacking interaction and electrostatic attraction. The as-prepared composite membranes showed high rejection rates (˃70%) toward small metal ions such as sodium (Na+) and magnesium (Mg2+) ions. In addition, both membranes also showed high rejection rates ˃99% for nickel (Ni2+) and lead (Pb2+) ions with good water permeability of 275 ± 10 L m-2 h-1 bar-1. We believe that our fabricated membranes will have a bright future in next generation desalination and water purification membranes.

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles.

Psoriasis is a skin disease which results in scales on the skin caused by flaky patches. Psoriasis is triggered by various conditions such as drug reactions, trauma, and skin infection etc. Globally, there are 125 million people affected by psoriasis and yet there is no effective treatment available, and it emphasizes the need for discovery of efficacious treatments. De-novo drug development takes 10-17 years and $2-$3 billion of investment with <10 % success rate to bring drug from concept to a market ready product. A possible alternative is drug repurposing, which aims at finding other indications of already approved drugs. In this study, a computational drug repurposing framework is developed and applied to differential gene expressions of Psoriasis targets obtained from the publicly available database (GEO). This strategy uses the gene expression signatures of the Psoriasis and compares it with perturbagen available in the CMap. Based on the connected signature drugs are ranked which could possibly reverse the signatures to stop the psoriasis. The drugs with most negative connectivity scores are ranked efficient and vice versa. The top hit drugs are verified using the literature survey of the peer reviewed journal, electronic health records, patents, and hospital database. As a result, 50/150 and 37/150 drugs are confirmed to have anti-psoriasis efficacy in two datasets. Top 10 drugs are suggested as potential repurposable drugs for psoriasis. This study offers, a powerful yet simple approach for rapid identification of potential drug repurposing candidates in Psoriasis and any disease of interest.

Functionalized Graphene Oxide-Based Lamellar Membranes with Tunable Nanochannels for Ionic and Molecular Separation.

Graphene oxide (GO)-based membranes with tunable microstructure and controlled nanochannels have attracted an increasing interest for various applications in wastewater treatment, desalination, gas separation, organic nanofiltration, etc. However, they showed limited use in water desalination due to their lower stability and separation efficiency. In this work, a class of two-dimensional (2D) GO lamellar membranes have been prepared with controlled pores for efficient and fast separation of ions and dye molecules. The GO membranes are fucntionalized with a star-like 6-armed poly(ethylene oxide) using the simple amidation route under mild conditions. The as-prepared covalently cross-linked networks are chemically steady in aqueous medium and show remarkable selectivity (∼100%) for several probe molecules and 10-100 higher permeance than those of the reported GO-based membranes. Further, such membranes are also used for salt separation and show more than 80% rejection for Pb2+ and Ni2+ salts. Moreover, a 1360 nm-thick membrane shows >99% rejection for NaCl with a good water permeance of up to 120 L m-2 h-1 bar-1. Additionally, these membranes are stable for more than 20 days under different conditions.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Silicone Elastomer Composites Fabricated with MgO and MgO-Multi-Wall Carbon Nanotubes with Improved Thermal Conductivity.

The effect of multiwall carbon nanotubes (MWCNTs) and magnesium oxide (MgO) on the thermal conductivity of MWCNTs and MgO-reinforced silicone rubber was studied. The increment of thermal conductivity was found to be linear with respect to increased loading of MgO. In order to improve the thermal transportation of phonons 0.3 wt % and 0.5 wt % of MWCNTs were added as filler to MgO-reinforced silicone rubber. The MWCNTs were functionalized by hydrogen peroxide (H2O2) to activate organic groups onto the surface of MWCNTs. These functional groups improved the compatibility and adhesion and act as bridging agents between MWCNTs and silicone elastomer, resulting in the formation of active conductive pathways between MgO and MWCNTs in the silicone elastomer. The surface functionalization was confirmed with XRD and FTIR spectroscopy. Raman spectroscopy confirms the pristine structure of MWCNTs after oxidation with H2O2. The thermal conductivity is improved to 1 W/m·K with the addition of 20 vol% with 0.5 wt % of MWCNTs, which is an ~8-fold increment in comparison to neat elastomer. Improved thermal conductive properties of MgO-MWCNTs elastomer composite will be a potential replacement for conventional thermal interface materials.

Two-Dimensional Transition Metal Carbides and Nitrides (MXenes) for Water Purification and Antibacterial Applications.

Two-dimensional (2D) materials such as graphene, graphene oxide (GO), metal carbides and nitrides (MXenes), transition metal dichalcogenides (TMDS), boron nitride (BN), and layered double hydroxide (LDH) metal-organic frameworks (MOFs) have been widely investigated as potential candidates in various separation applications because of their high mechanical strength, large surface area, ideal chemical and thermal stability, simplicity, ease of functionalization, environmental comparability, and good antibacterial performance. Recently, MXene as a new member of the 2D polymer family has attracted significant attention in water purification, desalination, gas separation, antibacterial, and antifouling applications. Herein, we review the most recent progress in the fabrication, preparation, and modification methods of MXene-based lamellar membranes with the emphasis on applications for water purification and desalination. Moreover, the antibacterial properties of MXene-based membranes show a significant potential for commercial use in water purification. Thus, this review provides a directional guide for future development in this emerging technology.