Discovery of small molecules from natural compound databases as potent retinoid X alpha receptor agonists to treat Alzheimer's disease

Alzheimer's is characterized as a progressive neurodegenerative disease due to beta-amyloid accumulation in the brain. Some previous studies reported that RXR agonists could be effective in the treatment of Alzheimer's disease. There are currently numerous attempts being made to discover a natural RXR agonist that is more potent than 9-cis-retinoic acid (9CR). One of the most efficient resources for finding high-potential compounds is natural databases. In this study, 81215 compounds from the IB screen library as natural databases were docked against the RXR-alpha binding site. The best compounds discovered interact with the RXR-alpha binding site with a lower binding energy (-11 to -13 kcal/mol) than the binding energy of -10.94 kcal/mol for 9-cis, which means that these compounds could interact stronger with RXR-alpha than 9CR. All selected compounds could pass the blood-brain barrier. Physiochemical properties assessment indicated that all compounds passed Lipinski's rule and had the potential to be oral drug candidates. The stability of protein-ligand complexes during a timescale of 100 ns by Molecular Dynamics simulation demonstrated that all compounds could effectively interact with the RXR binding site. The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) represented that all selected hit compounds had a better binding affinity to the alpha RXR binding site compared to 9CR, which means these hit compounds had potential drug candidates for the treatment of Alzheimer's disease. However, experimental assessment is needed to validate this result.Communicated by Ramaswamy H. Sarma.

Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation

Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.

A comprehensive bioinformatic analysis revealed novel MicroRNA biomarkers of Parkinson's disease

Parkinson's disease (PD) is categorized as a neurodegenerative disorder. Different studies have focused on the role of microRNAs (miRNAs) on PD progression. Due to its complexity in initiation and progression, a considerable requirement has arisen to identify novel miRNA biomarkers in a noninvasive manner. In silico analysis has been used to select differentially expressed miRNAs (DE-miRNAs) and key pathways in this disease. In this manner, several data sets of different neurodegenerative diseases have been analyzed to purify the findings of the present study. Totally, 15 DE miRNAs showed significant changes compared to healthy controls and other neurodegenerative diseases. Then, the targets of the miRNAs were predicted through miRTarBase and TargetScan databases. Besides, enrichment analysis was implemented for predicted target genes. Most of the target genes were enriched in the TRAIL signaling pathway, Regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism, protein serine/threonine kinase activity, and Cytoplasm. Moreover, a protein-protein interaction network was constructed to find the most key DE miRNAs and targets in this disease. The results of the present study may help researchers shed light on the discovery of novel biomarkers for PD.

The association between interferon lambda 3 and 4 gene single-nucleotide polymorphisms and the recovery of COVID-19 patients

BACKGROUND

The recent pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has elevated several clinical and scientific questions. These include how host genetic factors influence the pathogenesis and disease susceptibility. Therefore, the aim of this study was to evaluate the impact of interferon lambda 3 and 4 (IFNL3/4) gene polymorphisms and clinical parameters on the resistance and susceptibility to coronavirus disease 2019 (COVID-19) infection.

METHODS

A total of 750 SARS-CoV-2 positive patients (375 survivors and 375 nonsurvivors) were included in this study. All single-nucleotide polymorphisms (SNPs) on IFNL3 (rs12979860, rs8099917, and rs12980275) and IFNL4 rs368234815 were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.

RESULTS

In this study, a higher viral load (low PCR Ct value) was shown in nonsurvivor patients. In survivor patients, the frequency of the favorable genotypes of IFNL3/4 SNPs (rs12979860 CC, rs12980275 AA, rs8099917 TT, and rs368234815 TT/TT) was significantly higher than in nonsurvivor patients. Multivariate logistic regression analysis has shown that a higher low-density lipoprotein (LDL), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and PCR Ct value, and lower 25-hydroxyvitamin D, and also IFNL3 rs12979860 TT, IFNL3 rs8099917 GG, IFNL3 rs12980275 GG, and IFNL4 rs368234815 ∆G/∆G genotypes were associated with the severity of COVID-19 infection.

CONCLUSIONS

The results of this study proved that the severity of COVID-19 infection was associated with clinical parameters and unfavorable genotypes of IFNL3/IFNL4 SNPs. Further studies in different parts of the world are needed to show the relationship between severity of COVID-19 infection and host genetic factors.

Novel adsorptive PVC nanofibrous/thiol-functionalized TNT composite UF membranes for effective dynamic removal of heavy metal ions

The development of efficient strategies for the removal of heavy metal ions from aqueous solutions is rapidly demanded as these contaminants are very toxic and carcinogenic and show detrimental effects on the living creatures. The main focus of the current study is on the preparation and assessment of electrospun adsorptive nanofiber membranes for the removal of toxic Ni(II) and Cu(II) from wastewater in the ultrafiltration process. Hydrothermally synthesized titanate nanotubes (TNT) was modified with thiol functional groups and then directly incorporated to the polyvinyl chloride nanofiber matrices via electrospinning process to fabricate an adsorptive membrane. The as-prepared electrospun nanofiber membranes and the nanoadsorbents were characterized with respect to the physiochemical properties, surface structure and morphology, applying XPS, FTIR, FESEM, EDX and TEM analysis and then, the membranes were evaluated in terms of the removal of the heavy metal ions in a continuous ultrafiltration mode. In adsorptive filtration of the metal ions, the effective factors including nanoadsorbents loading (0.5-1.5 wt%), initial metal ion concentration (60-150 mg/L), feed temperature (~25 °C-45 °C), presence of competing ion and reusability were investigated in the UF system where the membranes containing 1.5 wt% thiol-modified TNT and virgin TNT adsorbents demonstrated excellent removal efficiency compared to the other membranes. The Cu(II) and Ni(II) removal efficiency of the membrane containing 1.5 wt% functionalized TNT was 90% and 86.7%, respectively which was the highest ones. As was expected and due to the uniform dispersion and less aggregation of the modified TNT adsorbents on the large surface area of the electrospun nanofibers, more adsorption capacity of the nanoparticles can be exploited. Moreover, the strong affinity of the thiol functional groups toward the metal cations, these membranes removed metal contaminants more efficiently. Besides, the Cu(II) removal efficiency of the fabricated membranes didn't show any drastic changes in the presence of the competing ions. Furthermore, acceptable performance was achieved for the prepared membranes even after four adsorption/regeneration cycles in the continuous UF experiments, demonstrating the feasibility and effectiveness of the prepared adsorptive nanofiber membranes for the removal of heavy metal ions.

[A novel human bone morphogenetic protein-7 variant with an enriched heparin-binding site]

BMPs are osteoinductive proteins which are used in treatment of acute fractures. Large quantities of recombinant proteins are usually needed to achieve efficacy in the clinic. This translates to severe complications and high costs. Different strategies have been developed to improve the efficacy and safety of BMPs. Modification of the heparin-binding site in order to increase the local retention time of the morphogen is one of these approaches. Aiming at further improvement in properties of BMP-7, a novel form of this protein was designed and expressed successfully in Chinese Hamster Ovarian (CHO) cells. Substitution of the Bone morphogenetic protein-7 N-terminus by the heparin-binding site of Bone morphogenetic protein-2 was carried out to increase the heparin binding capacity of the novel protein. It was found that the novel variant, retained its in vitro biological activity and the heparin binding capacity of this protein was approximately 20% higher than that of the wild-type at a protein concentration of 100 ng/mL. The novel protein as the first variant of hBMP-7 with the enriched heparin-binding site may offer more advantages in clinical use as compared to the existing commercial form.

Production of Chimeric Recombinant Single Domain Antibody—Green Fluorescent Fusion Protein in Chinese Hamster Ovary Cells

There is an increasing interest in the application of nanobodies such as VHH in the field of therapy and imaging. In the present study a stable genetically engineered cell line of Chinese hamster ovary (CHO) origin transfected using two sets of expression vectors was constructed in order to permit the cytoplasmic and extracellular expression of single domain antibody along with green fluorescent protein (GFP) as reporter gene. The quality of the constructs were examined both by the restriction map as well as sequence analysis. The gene transfection and protein expression was further examined by reverse transcription-polymerase chain reaction (RT-PCR). The transfected cells were grown in 200 microg/mL hygromycin containing media and the stable cell line obtained showed fluorescent activity for more than a period of 180 days. The production of fusion protein was also detected by fluorescent microscopy, fluorescent spectroscopy as well as by enzyme-linked immunosorbent assay (ELISA) analysis. This strategy allows a rapid production of recombinant fluobodies involving VHH, which can be used in various experiments such as imaging and detection in which a primary labeled antibody is required.