Biomimetic Slippery Surface with Exclusive Liquid-Repellent and Self-Cleaning Properties for Antifouling.

The nature always offers amazing inspiration, where it is highly desirable to endow coatings on marine equipment with powerful functions. An excellent example is slippery zone of Nepenthes pitcher, which possesses novel liquid-repellent and self-cleaning performance. Therefore, this study presents an efficient fabrication method to prepare a novel coating. The coatings were fabricated by designing biomimetic textures extracted from the lunate bodies of slippery zone on polydimethylsiloxane (PDMS) and then grafting Dictyophora indusiata polysaccharide (DIP) modifier. The as-prepared slippery coatings exhibited outstanding antifouling properties against kinds of daily life pollutants such as Chlorella and coffee. This synergistic strategy was proposed combined with environmentally friendly modifier grafting and heterogeneous microstructure on the surface to broaden new probabilities for manufacturing slippery coatings with incredible protective functionality.

Synthesis and evaluation of pyridine-3-carboxamide analogs as effective agents against bacterial wilt in tomatoes.

This study focused on developing novel pyridine-3-carboxamide analogs to treat bacterial wilt in tomatoes caused by Ralstonia solanacearum. The analogs were synthesized through a multistep process and their structures confirmed using spectroscopy. Molecular docking studies identified the most potent analog from the series. A specific analog, compound 4a, was found to significantly enhance disease resistance in tomato plants infected with R. solanacearum. The structure-activity relationship analysis showed the positions and types of substituents on the aromatic rings of compounds 4a-i strongly influenced their biological activity. Compound 4a, with a chloro group at the para position on ring C and hydroxyl group at the ortho position on ring A, was exceptionally effective against R. solanacearum. When used to treat seeds, the analogs displayed remarkable efficacy, especially compound 4a which had specific activity against bacterial wilt pathogens. Compound 4a also promoted vegetative and reproductive growth of tomato plants, increasing seed germination and seedling vigor. In plants mechanically infected with bacteria, compound 4a substantially reduced the percentage of infection, pathogen quantity in young tissue, and disease progression. The analogs were highly potent due to their amide linkage. Molecular docking identified the best compounds with strong binding affinities. Overall, the strategic design and synthesis of these pyridine-3-carboxamide analogs offers an effective approach to targeting and controlling R. solanacearum and bacterial wilt in tomatoes.

Influences of emerging drying technologies on rice quality.

Rice is an important staple food in the world. Drying is an important step in the post-harvest handling of rice and can influence rice qualities and thus play a key role in determining rice commercial and nutritional value. In rice processing, traditional drying methods may lead to longer drying times, greater energy consumption, and unintended quality losses. Thus, it is imperative to improve the physical, chemical, and milling properties of rice while preserving its nutritional value, flavor, and appearance as much as possible. Additionally, it is necessary to increase the efficiency with which heat energy is utilized during the thermal processing of freshly harvested paddy. Moreover, this review provides insights into the current application status of six different innovative drying technologies such as radio frequency (RF) drying, microwave (MW) drying, infrared (IR) drying, vacuum drying (VD), superheated steam (SHS) drying, fluidized bed (FB) drying along with their effect on the quality of rice such as color, flavor, crack ratio, microstructure and morphology, bioactive components and antioxidant activity as well asstarch content and glycemic index. Dielectric methods of drying due to volumetric heating results in enhanced drying rate, improved heating uniformity, reduced crack ratio, increased head rice yield and better maintain taste value of paddy grains. These novel emerging drying techniques increased the interactions between hydrated proteins and swollen starch granules, resulting in enhanced viscosity of rice flour and promoted starch gelatinization and enhanced antioxidant activity which is helpful to produce functional rice. Moreover, this review not only highlights the existing challenges posed by these innovative thermal technologies but also presents potential solutions. Additionally, the combination of these technologies to optimize operating conditions can further boost their effectiveness in enhancing the drying process. Nevertheless, future studies are essential to gain a deeper understanding of the mechanism of quality changes induced by emerging processing technologies. This knowledge will help expand the application of these techniques in the rice processing industry.

AI-driven Discovery of Celecoxib and Dexamethasone for Exploring their Mode of Action as Human Interleukin (IL-6) Inhibitors to Treat COVID-19-induced Cytokine Storm in Humans.

BACKGROUND: In the case of COVID-19 patients, it has been observed that the immune system of the infected person exhibits an extreme inflammatory response known as cytokine release syndrome (CRS) where the inflammatory cytokines are swiftly produced in quite large amounts in response to infective stimuli. Numerous case studies of COVID-19 patients with severe symptoms have documented the presence of higher plasma concentrations of human interleukin-6 (IL-6), which suggests that IL-6 is a crucial factor in the pathophysiology of the disease. In order to prevent CRS in COVID-19 patients, the drugs that can exhibit binding interactions with IL-6 and block the signaling pathways to decrease the IL-6 activity may be repurposed. METHODS: This research work focused on molecular docking-based screening of the drugs celecoxib (CXB) and dexamethasone (DME) to explore their potential to interact with the binding sites of IL-6 protein and reduce the hyper-activation of IL-6 in the infected personnel. RESULTS: Both of the drugs were observed to bind with the IL-6 (IL-6 receptor alpha chain) and IL-6Rα receptor with the respective affinities of -7.3 kcal/mol and -6.3 kcal/mol, respectively, for CXB and DME. Moreover, various types of binding interactions of the drugs with the target proteins were also observed in the docking studies. The dynamic behaviors of IL-6/IL-6Rα in complex with the drugs were also explored through molecular dynamics simulation analysis. The results indicated significant stabilities of the acquired drug-protein complexes up to 100 ns. CONCLUSION: The findings of this study have suggested the potential of the drugs studied to be utilized as antagonists for countering CRS in COVID-19 ailment. This study presents the studied drugs as promising candidates both for the clinical and pre-clinical treatment of COVID-19.

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective.

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.

Identification of novel scaffolds targeting SIRT3 through molecular modeling techniques for the treatment of Hepatocellular carcinoma.

Hepatocellular carcinoma is one of the top causes of cancer-related death globally. SIRT3 belongs to the Sirtuin family of proteins, a collection of NAD+-dependent enzymes that play a role in controlling several cellular functions, including metabolism, aging, and stress response. SIRT3 expression has been discovered to be often downregulated in HCC tissues relative to normal liver tissues. Hence, SIRT3 may function as a tumor suppressor in HCC. In the present study, pharmacophore-based virtual screening of a small molecule database was performed initially, and then the screened hits were docked to the active site of SIRT3 to choose the best binding modes. One co-crystal ligand (PDB name: 1NQ) was utilized as a template to generate pharmacophore model query. A total of 0.2 million compounds from the VITAS-M Lab database were downloaded and prepared for virtual screening. Following database preparation, ligand-based virtual screening was performed using the pharmacophore query model generated in the previous phase. The compounds with the same pharmacophoric characteristics as the query at the same distance were screened. There were a total of 74 hits that matched the query model. These compounds were then docked to the SIRT3 using the standard precision protocol of the glide tool. To select hits with high binding affinities, a threshold of -8 kcal/mol was used. Based on the glide gscore, two hits were chosen. These two hits were selected to investigate the stability of the protein-ligand complex by molecular dynamics simulation. All of these findings indicate that the selected hit compounds C1 and C2 can serve as lead compounds in inhibiting the biological activity of SIRT3 requiring further detailed investigations.Communicated by Ramaswamy H. Sarma.

Computer-aided identification of dengue virus NS2B/NS3 protease inhibitors: an integrated molecular modelling approach for screening of phytochemicals.

Globally, dengue (DENV) fever has appeared as the most widespread vector-borne disease, affecting more than 100 million individuals annually. No approved anti-DENV therapy or preventive vaccine is available yet. DENV NS3 protein is associated with protease activity and is essential for viral replication process within the host cell. NS2B is linked with NS3 protein as a cofactor. Hence, NS3/NS2B is a potential druggable target for developing inhibitors against dengue virus. In the present study, a dataset of Beta vulgaris L.-based natural compounds was developed. Virtual ligand screening of 30 phytochemicals was carried out to find novel inhibitors against the NS2B/NS3 protein. Spatial affinity, drug-likeness, and binding behaviors of selected phytochemicals were analyzed. Post-simulation analysis, including Principal Component Analysis (PCA), MMGBSA, and Co-relation analysis, was also performed to provide deep insight for elucidating protein-ligand complexes. This computer-aided screening scrutinized four potent phytochemicals, including betavulgaroside II, vitexin xyloside, epicatechin, and isovitexin2-O-xyloside inhibitors exhibiting optimal binding with viral NS3/NS2B protein. Our study brings novel scaffolds against DENV NS2B/NS3 of serotype-2 to act as lead molecules for further biological optimization. In future, this study will prompt the exploration and development of adjuvant anti-DENV therapy based on natural compounds.Communicated by Ramaswamy H. Sarma.

An overview of genome engineering in plants, including its scope, technologies, progress and grand challenges.

Genome editing is a useful, adaptable, and favored technique for both functional genomics and crop enhancement. Over the years, rapidly evolving genome editing technologies, including clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas), transcription activator-like effector nucleases (TALENs), and zinc finger nucleases (ZFNs), have shown broad application prospects in gene function research and improvement of critical agronomic traits in many crops. These technologies have also opened up opportunities for plant breeding. These techniques provide excellent chances for the quick modification of crops and the advancement of plant science in the future. The current review describes various genome editing techniques and how they function, particularly CRISPR/Cas9 systems, which can contribute significantly to the most accurate characterization of genomic rearrangement and plant gene functions as well as the enhancement of critical traits in field crops. To accelerate the use of gene-editing technologies for crop enhancement, the speed editing strategy of gene-family members was designed. As it permits genome editing in numerous biological systems, the CRISPR technology provides a valuable edge in this regard that particularly captures the attention of scientists.

Clinical studies investigating the role of mesenchymal stem cells in healing of fracture non-unions: a systematic review.

Objectives: To identify and evaluate the effectiveness of mesenchymal stem cells (MSCs) in augmenting healing in fracture non-unions. METHODS: A focused literature search was performed on the PubMed/MEDLINE index using the keywords: "non-union", "mesenchymal stem cells", "bone healing", "MSC", "stem cells", and their MeSH terms. The search was reiterated until the 10th of August 2022. Clinical studies were included that assessed the effect of MSCs on fracture non-unions. RESULTS: Thirteen human clinical trials, studying a total of 318 participants were identified and studied. MSCs with and without biological or synthetic scaffolds were found to be effective in healing of non-unions. CONCLUSIONS: MSCs has been demonstrated to have promising outcomes in the treatment of bone non-union and tissue engineering methods utilizing MSCs may well prove to be valuable in accelerating the process of bone union. However, clinical application of MSCs as a standard method in achieving union in fracture non-unions requires larger clinical trials with a standardised approach to analyzing outcomes.

Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda.

As remote work has become more common than ever throughout the COVID-19 pandemic, it has drawn special attention from scholars. However, the outcome has been significantly sporadic and fragmented. In our systematic review, we use artificial intelligence-based machine learning tools to examine the relevant extant literature in terms of its dominant topics, diversity, and dynamics. Our results identify-eight research themes: (1) Effect on employees at a personal level, (2) Effect on employees' careers, (3) Family life and gender roles, (4) Health, well-being, and safety, (5) Labor market dynamics, (6) Economic implications, (7) Remote work management, (8) Organizational remote work strategies. With further content analysis, we structure the sporadic research into three overarching categories. Finally, for each category, we offer a detailed agenda for further research.

Integrating Pharmacological and Computational Approaches for the Phytochemical Analysis of Syzygium cumini and Its Anti-Diabetic Potential.

Diabetes mellitus (DM) is a metabolic disease caused by improper insulin secretion leading to hyperglycemia. Syzygium cumini has excellent therapeutic properties due to its high levels of phytochemicals. The current research aimed to evaluate the anti-diabetic potential of S. cumini plant's seeds and the top two phytochemicals (kaempferol and gallic acid) were selected for further analysis. These phytochemicals were selected via computational tools and evaluated for α-Glucosidase inhibitory activity via enzymatic assay. Gallic acid (IC50 0.37 µM) and kaempferol (IC50 0.87 µM) have shown a stronger α-glucosidase inhibitory capacity than acarbose (5.26 µM). In addition, these phytochemicals demonstrated the highest binding energy, hydrogen bonding, protein-ligand interaction and the best MD simulation results at 100 ns compared to acarbose. Furthermore, the ADMET properties of gallic acid and kaempferol also fulfilled the safety criteria. Thus, it was concluded that S. cumini could potentially be used to treat DM. The potential bioactive molecules identified in this study (kaempferol and gallic acid) may be used as lead drugs against diabetes.

In-silico elucidation reveals potential phytochemicals against angiotensin-converting enzyme 2 (ACE-2) receptor to fight coronavirus disease 2019 (COVID-19).

The coronavirus (SARS-CoV-2) pandemic is rapidly advancing and spreading worldwide, which poses an urgent need to develop anti-SARS-CoV-2 agents. A human receptor, namely, angiotensin-converting enzyme 2 (ACE-2), supports the SARS-CoV-2 entry, therefore, serves as a target for intervention via drug. In the current study, bioinformatic approaches were employed to screen potent bioactive compounds that might be ACE-2 receptor inhibitors. The employment of a docking study using ACE receptor protein with a ready-to-dock database of phytochemicals via MOE software revealed five compounds as potent molecules. Among them, astragaloside exhibited the highest binding affinity -21.8 kcal/mol and stable interactions within the active site of the ACE-2 receptor. Similarly, the phytochemicals such as pterocaryanin B, isoastragaloside II, and astraisoflavan glucoside followed by oleuropein showed a stronger binding affinity. We hypothesize these compounds as potential lead candidates for the development of anti- COVID-19 target-specific drugs.

The Insight of In Silico and In Vitro evaluation of Beta vulgaris phytochemicals against Alzheimer's disease targeting acetylcholinesterase.

B. vulgaris extracts possess antioxidant, anti-inflammatory along with its role in improving memory disorders. Subsequently, in vitro and in silico studies of its purified phytochemicals may expand complementary and alternative Alzheimer's therapeutic option. Super activation of acetylcholinesterase enzyme is associated explicitly with Alzheimer's disease (AD) ultimately resulting in senile dementia. Hence, acetylcholinesterase enzyme inhibition is employed as a promising approach for AD treatment. Many FDA approved drugs are unable to cure the disease progression completely. The Present study was devised to explore the potential bioactive phytochemicals of B. vulgaris as alternative therapeutic agents against AD by conducting in vitro and in silico studies. To achieve this, chemical structures of phytochemicals were recruited from PubChem. Further, these compounds were analyzed for their binding affinities towards acetylcholinesterase (AChE) enzyme. Pharmacophoric ligand-based models showed major characteristics like, HBA, HBD, hydrophobicity, aromaticity and positively ionizable surface morphology for receptor binding. Virtual screening identified three hit compounds including betanin, myricetin and folic acid with least binding score compared to the reference drug, donepezil (-17 kcal/mol). Further, in vitro studies for anti-acetylcholinesterase activity of betanin and glycine betaine were performed. Dose response analysis showed 1.271 µM and 1.203 µM 50% inhibitory concentration (IC50) values for betanin and glycine betaine compounds respectively. Our findings indicate that phytoconstituents of B. vulgaris can be implicated as an alternative therapeutic drug candidate for cognitive disorders like Alzheimer's disease.

Compatibility and synergistic interactions of fungi, Metarhizium anisopliae, and insecticide combinations against the cotton aphid, Aphis gossypii Glover (Hemiptera: Aphididae).

Aphids are major pests affecting cereals, vegetables, fruit, forestry and horticultural produce. A multimodal approach may be an effective route to controlling this prolific pest. We assessed the individual and combined effect of eight insecticides and the entomopathogenic fungi, Metarhizium anisopliae (Metschin.) against the cotton aphid, Aphis gossypii Glover (Hemiptera: Aphididae), under laboratory conditions. Six of the insecticides tested were found to be highly compatible (flonicamid, imidacloprid, nitenpyram, dinotefuran, pyriproxyfen and spirotetramat), showing positive integration with the fungus and were selected for bioassays. The combination mixtures (1:1 ratio of M. anisopliae: insecticide) were significantly more toxic to A. gossypii than individual treatments. Maximum mortality (91.68%) of A. gossypii was recorded with combination of flonicamid and M. anisopliae (2.4 × 106 cfu/ml) 72 h after application. While minimum mortality (17.08%) was observed with the individual treatment of M. anisopliae (2.4 × 106 cfu/ml). The insecticides revealed toxicity consistent with their compatibility with M. anisopliae, ranking for efficacy exactly as they did for compatibility. In addition, the synergy factor (SF) and co-toxicity coefficient (CTC) values indicated synergistic interactions at different time intervals. The synergistic efficacy revealed the potential of fungus-insecticide integration against sucking insect pests.

Berberis lyceum and Fumaria indica: in vitro cytotoxicity, antioxidant activity, and in silico screening of their selected phytochemicals as novel hepatitis C virus nonstructural protein 5A inhibitors.

Berberis lyceum and Fumaria indica are two Pakistani indigenous herbal medicines used to treat liver infections, including hepatitis C virus (HCV). This study aimed to evaluate the cytotoxicity, and antioxidant activity of these plant extracts and computationally screen their selected phytoconstituents as HCV NS5A inhibitors. The viability of HepG2 cells was assessed 24 h and 48 h post-treatment using colorimetric and dye exclusion methods. Antioxidant properties were examined by the 2,2-diphenyl-1-picrylhydrazyl (DPPH), reducing power, and total antioxidant capacity assays. Seventeen known phytochemicals identified from each plant were docked into the active binding site of HCV NS5A protein. The top hit ligands were analyzed for their druglikeness properties and the indices of absorption, distribution, metabolism, elimination, and toxicity (ADMET). The results showed that both plant extracts were non-toxic (CC50 > 200 µg/ml). The IC50 values of DPPH-radical scavenging activity were 51.02 ± 0.94 and 62.91 ± 1.85 µg/ml for B. lyceum and F. indica, respectively. They also exhibited reducing power and total antioxidant capacity.The phytochemicals were identified as potent HCV NS5A inhibitors with good druglikeness and ADMET properties. Six of the docked phytochemicals exhibited higher binding scores (-17.9 to -19.2 kcal/mol) with HCV NS5A protein than the standard drug, daclatasvir (-17.2 kcal/mol). Molecular dynamics (MD) simulation confirmed the stability of two compounds, berbamine and paprafumine at 100 ns with active site of HCV NS5A protein. The identified compounds through molecular docking and MD simulation could have potential as HCV NS5A inhibitor after further validation.

Implementation of Vaccinomics and In-Silico Approaches to Construct Multimeric Based Vaccine Against Ovarian Cancer.

One of the most common gynecologic cancers is ovarian cancer and ranked third after the other two most common cancers: cervical and uterine. The highest mortality rate has been observed in the case of ovarian cancer. To treat ovarian cancer, an immune-informatics approach was used to design a multi-epitope vaccine (MEV) structure. Epitopes prediction of the cancer testis antigens (NY-ESO-1), A-Kinase anchor protein (AKAP4), Acrosin binding protein (ACRBP), Piwi-like protein (PIWIL3), and cancer testis antigen 2 (LAGE-1) was done. Non-toxic, highly antigenic, non-allergenic, and overlapping epitopes were shortlisted for vaccine construction. Chosen T-cell epitopes displayed a robust binding attraction with their corresponding Human Leukocyte Antigen (HLA) alleles demonstrated 97.59% of population coverage. The vaccine peptide was established by uniting three key constituents, comprising the 14 epitopes of CD8 + cytotoxic T lymphocytes (CTLs), 5 helper epitopes, and the adjuvant. For the generation of the effective response of CD4 + cells towards the T-helper cells, granulocyte-macrophage-colony-stimulating factor (GM-CSF) was applied. With the addition of adjuvants and linkers, the construct size was 547 amino acids. The developed MEV structure was predicted to be antigenic, non-toxic, non-allergenic, and firm in nature. I-tasser anticipated the 3D construction of MEV. Moreover, disulfide engineering further enhanced the stability of the final vaccine protein. In-silico cloning and vaccine codon optimization were done to analyze the up-regulation of its expression. The outcomes established the vaccine's immunogenicity and safety profile, besides its aptitude to encourage both humoral and cellular immune responses. The offered vaccine, grounded on our in-silico investigation, may be considered for ovarian cancer immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10989-021-10294-w.

Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis.

OBJECTIVE: Incidence of both Type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC) are rapidly increasing worldwide. One of the leading causes of HCC is hepatitis C virus (HCV), which is a resource of blood-borne viral infection. HCV increases the risk for HCC probably by promoting fibrosis and cirrhosis. Association among T2DM and HCV related HCC remains significant, indicating that such association is clinically reliable and robust. Lawson was the first who uncovered HCC in person suffered from T2DM. Until now, genetic association between HCV related HCC and T2DM is poorly known. Current work was designed to figure out the molecular mechanisms of both diseases by identifying the hub genes and therapeutic drugs using integrated bioinformatics analysis. METHODS: Four microarray datasets were downloaded from GEO database and analyzed using R in order to obtain different expressed genes (DEGs). Protein-protein interaction (PPI) networks was constructed using STRING tool and visualized by Cytoscape. Moreover, hub genes were identified on the basis of their degree of connectivity. Finally, Networkanalyst and DGIdb were used for the identification of transcription factors (TFs) and selection of candidate drugs, respectively. RESULTS: A total of 53 DEGs were identified, of which 41 were upregulated genes and 12 were downregulated genes. PPI network obtained from STRING were subjected to Cytoscape plugin cytoHubba, and top 10 genes (AURKA, JUN, AR, MELK, NCOA2, CENPF, NCAPG, PCK1, RAD51AP1, and GTSE1) were chosen as the target hub genes based on the highest degree of connectivity. Furthermore, 47 drugs of AURKA, JUN, AR, MELK, and NCOA2 were found having therapeutic potential to treat HCV-HCC in patients with T2DM. CONCLUSION: This study updates the information and yield a new perspective in context of understanding the pathogenesis and development of HCV related HCC in affected persons with T2DM. In vivo and in vitro investigation of hub genes and pathway interaction is essential to delineate the specific roles of the novel hub genes, which may help to reveal the genetic association between HCV-HCC and T2DM. In future, hub genes along with their candidate drugs might be capable of improving the personalized detection and therapies for both diseases.

Anti-aging Natural Compounds and their Role in the Regulation of Metabolic Pathways Leading to Longevity.

Aging is an unavoidable process, leading to cell senescence due to physiochemical changes in an organism. Anti-aging remedies have always been of great interest since ancient times. The purpose of anti-aging activities is to increase the life span and the quality of life. Anti-aging activities are primarily involved in the therapies of age-related disorders such as Parkinson's Disease (PD), Alzheimer's Disease (AD), cardiovascular diseases, cancer, and chronic obstructive pulmonary diseases. These diseases are triggered by multiple factors that are involved in numerous molecular pathways including telomere shortening, NF-κB pathway, adiponectin receptor pathway, insulin, and IGF signaling pathway, AMPK, mTOR, and mitochondria dysfunction. Natural products are known as effective molecules to delay the aging process through influencing metabolic pathways and thus ensure an extended lifespan. These natural compounds are being utilized in drug design and development through computational and high throughput techniques for effective pro-longevity drugs. A comprehensive study on natural compounds demonstrating their anti-aging activities along with databases of natural products for drug designing was executed and summarized in this review article.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.

BACKGROUND: Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2. METHODS: In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis. RESULTS: The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant. CONCLUSION: Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Efficacy of biorational insecticides against Bemisia tabaci (Genn.) and their selectivity for its parasitoid Encarsia formosa Gahan on Bt cotton.

The toxicity of seven biorational insecticides [five insect growth regulators (Buprofezin, Fenoxycarb, Pyriproxyfen, Methoxyfenozide, and Tebufenozide) and two oil-extracts of neem and bitter gourd seeds] against Bemisia tabaci and their selectivity for its parasitoid, Encarsia formosa were evaluated in laboratory and field conditions for 2 years (2018-2019) in Pakistan. Toxicity results demonstrate that Pyriproxyfen, Buprofezin, and Fenoxycarb proved to be effective (80-91% mortality and 66.3-84.2% population-reduction) against B. tabaci followed by Methoxyfenozide, Tebufenozide (50-75% mortality and 47.8-52.4% population-reduction), and then oil-extracts of neem and bitter gourd (25-50% mortality and 36.5-39.8% population-reduction) in the laboratory [72 h post-application exposure interval (PAEI)] and field trails (168 h PAEI), respectively. All tested biorationals, except Methoxyfenozide [(slightly-harmful/Class-II), i.e., causing mortality of parasitoids between a range of 25-50%] and Tebufenozide [(moderately-harmful/Class-III), i.e., causing mortality of parasitoids between the ranges of 51-75%], proved harmless/Class-I biorationals at PAEI of 7-days in the field (parasitism-reduction < 25%) and 3-days in the lab (effect < 30%). In laboratory bioassays, exposure of parasitized-pseudopupae and adult-parasitoids to neem and bitter gourd oils demonstrated that these compounds proved harmless/Class-I biorationals (< 30% mortality). Alternatively, Pyriproxyfen, Buprofezin, Fenoxycarb, Methoxyfenozide, and Tebufenozide were slightly-harmful biorationals (30-79% mortality) against the respective stages of E. formosa. We conclude that most of the tested biorationals proved harmless or slightly harmful to E. formosa, except tebufenozide after PAEI of 7-days (168 h) in the field and, therefore, may be used strategically in Integrated Pest Management (IPM) of B. tabaci.