Identification of Potential Ligands of the Main Protease of Coronavirus SARS-CoV-2 (2019-nCoV) Using Multimodal Generative Neural-Networks

The recent outbreak of coronavirus disease 2019 (COVID-19) is posing a global threat to human population. The pandemic caused by novel coronavirus (2019-nCoV), also called as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2); first emerged in Wuhan city, Hubei province of China in December 2019. The rapid human to human transmission has caused the contagion to spread world-wide affecting 244,385,444 (244.4 million) people globally causing 4,961,489 (5 million) fatalities dated by 27 October 2021. At present, 6,697,607,393 (6.7 billion) vaccine doses have been administered dated by 27 October 2021, for the prevention of COVID-19 infections. Even so, this critical and threatening situation of pandemic and due to various variants’ emergence, the pandemic control has become challenging; this calls for gigantic efforts to find new potent drug candidates and effective therapeutic approaches against the virulent respiratory disease of COVID-19. In the respiratory morbidities of COVID-19, the functionally crucial drug target for the antiviral treatment could be the main protease/3-chymotrypsin protease (Mpro/3CLpro) enzyme that is primarily involved in viral maturation and replication. In view of this, in the current study I have designed a library of small molecules against the main protease (Mpro) of coronavirus SARS-CoV-2 (2019-nCoV) by using multimodal generative neural-networks. The scaffold-based molecular docking of the series of compounds at the active site of the protein was performed; binding poses of the molecules were evaluated and protein-ligand interaction studies followed by the binding affinity calculations validated the findings. I have identified a number of small promising lead compounds that could serve as potential inhibitors of the main protease (Mpro) enzyme of coronavirus SARS-CoV-2 (2019-nCoV). This study would serve as a step forward in the development of effective antiviral therapeutic agents against the COVID-19

Inpatient satisfaction at different public sector hospitals of a metropolitan city in Pakistan: A comparative cross-sectional study

Objective: To observe inpatient satisfaction at different public sector hospitals of Karachi, Pakistan.Methods: A cross sectional study was carried out during 2010-2012 in four major public sector hospitals of Karachi. A total of 710 patients completed the study. Responses were gathered in a self-structured questionnaire that comprised of four dimensions of satisfaction with doctor, staff, administration and treatment. Average Score of each dimension was taken and compared using one way analysis of variance.Result: Satisfaction with doctors, staff and administration of provincial and federal hospitals were comparatively similar (P \u3e 0.05). However, satisfaction with treatment significantly differed in all four hospitals (P \u3c 0.0001). Highest satisfaction with treatment was observed among inpatients of hospital running by medical institute (P \u3c 0.0001). Comparison with respect to different departments revealed significant difference for treatment satisfaction of medicine and surgery units. Patients who were admitted from emergency mode acquired lowest satisfaction in all aspects.Conclusion: Response of inpatients from public sector hospitals showed satisfaction with healthcare personnel and related administration. However, treatment dimension needs to be improved to get more satisfaction

Study the efficiency of single crystal CdTe/ZnCdS solar cell at various temperatures and illumination levels

CdTe is the best suited semiconductor for solar cells due to its band gap value 1.47 eV which is close to solar spectrum, low sublimation temperature and high absorption coefficient in the range of solar spectrum. To improve the photovoltaic performance of CdS/CdTe thin film solar cells, the CdS window layer is alloyed with different concentration of ZnS to reduce the resistivity and increase the band gap values. The single crystal CdTe based solar cell devices were prepared by vacuum evaporation method and have undergone for different temperature at various illumination levels to enhance the cell efficiency. We have achieved 14.37% efficiency and increased short circuit current density and open circuit voltage by reducing series resistance of the cell

OPTICAL AND STRUCTURAL PROPERTIES OF ZnxCd1-xS (X=0.2, 0.4, 0.6 AND 0.8): THIN FILMS PREPARED BY THERMAL EVAPORATION TECHNIQUE

Thin films of ZnxCd1-xS (x=0.2, 0.4, 0.6 and 0.8) were deposited on cleaned soda lime glass substrates at room temperature by thermal evaporation technique, having source current 50-65 Ampere, chamber pressure 10-5Torr and deposition rate 0.4 nm/sec. These conditions were same for all the thin films having different zinc concentrations. UV-VIS Spectrophotometry was used to study the optical properties of thin films of ZnxCd1-xS in room temperature. XRD was used to study the structure of the thin films of ZnxCd1-xS having various composition of „x‟. UV-VIS studies revealed that as the concentration of zinc content increases, transmission spectra shift towards the shorter wavelength region from (575-526)nm, the percent transmittance was increased in the visible range with the increase of zinc content, absorption edges and absorption coefficient spectra also shift towards the shorter wavelength and hence the direct band gap energy varied non-linearly from 2.55ev to 2.84ev.It was also found that optical conductivity increases with photon energy and thin film of Zn0.4Cd0.6S has high optical conductivity as compared to other value of „x‟. The reflectance and optical constants such as the extinction coefficient and refractive index were also found to depend upon the zinc concentration in the films. XRD studies showed that all the thin films of ZnxCd1-xS (x= 0.2, 0.4, 0.6 and 0.8) had a strong peak in between the diffraction angle 26.60- 31.70, which confirmed that all the thin films exhibited the wurtzite structure with a preferential orientation of (002) plane. It was also found that lattice constants, inters planer spacing, volume and grain size decreases except for Zn0.4Cd0.6S thin film which had high crystallinity as compared to the other composition of the zinc content

Synthesis, characterization, DPPH radical scavenging, urease enzyme inhibition, molecular docking simulation, and DFT analysis of imine derivatives of 4-formylpyridine with selective detection of Cu+2 Ions

This study aimed to prepare three imine derivatives (1, 2, and 3) via a condensation reaction of phenyl hydrazine, 2-hydrazino pyridine, and 4-methoxy aniline with 4-formyl pyridine. Electron impact mass spectrometry (EIMS), proton nuclear magnetic resonance (1H-NMR), ultraviolet-visible (UV-Vis) and Fourier transform infrared (FTIR) spectroscopy were utilized for the characterization. The chemosensing properties of [4((2-phenyl hydrazono)methyl) pyridine] (1), [2-(2-(pyridin-4-ylmethylene)hydrazinyl) pyridine] (2), and [4-methoxy-N-yl methylene) aniline] (3) imino bases have been explored for the first time in aqueous media. The photophysical properties of chemosensors (1, 2, and 3) were examined by various cations (Na+, NH4+, Ba+2, Ni+2, Ca+2, Hg+2, Cu+2, Mg+2, Mn+2, and Pd+2). The chemosensor (1) showed very selective binding capability with copper ions at low concentrations (20 μM) without the influence of any other mentioned ions. The maximum complexation was noted with Cu+2 and 1 at pH between 7 to 7.5. The stoichiometry binding ratio between chemosensor (1) and Cu+2 was determined by Job\u27s plot and it was found to be 1:2. The current study explored the use of these Schiff bases for the first time as heterocyclic chemosensors. DPPH radical scavenging, urease enzyme inhibition activities, molecular docking simulation, and density functional theory (DFT) analysis of compounds 1, 2, and 3 were also conducte

Discriminator-based adversarial networks for knowledge graph completion

Knowledge graphs (KGs) inherently lack reasoning ability which limits their effectiveness for tasks such as question-answering and query expansion. KG embedding (KGE) is a predominant approach where proximity between relations and entities in the embedding space is used for reasoning over KGs. Most existing KGE approaches use structural information of triplets and disregard contextual information which could be crucial to learning long-term relations between entities. Moreover, KGE approaches mostly use discriminative models which require both positive and negative samples to learn a decision boundary. KGs, by contrast, contain only positive samples, necessitating that negative samples are generated by replacing the head/tail of predicates with randomly-chosen entities. They are thus usually irrational and easily discriminable from positive samples, which can prevent the learning of sufficiently robust classifiers. To address the shortcomings, we propose to learn contextualized KGE using pretrained adversarial networks. We assume multi-hop relational paths(mh-RPs) as textual sequences for competitively learning discriminator-based KGE against the negative mh-RP generator. We use a pre-trained ELECTRA model and feed it with relational paths. We employ a generator to corrupt randomly-chosen entities with plausible alternatives and a discriminator to predict whether an entity is corrupted or not. We perform experiments on multiple benchmark knowledge graphs and the results show that our proposed KG-ELECTRA model outperforms BERT in link prediction