Search

DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection. [ABSTRACT FROM AUTHOR]

Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red–green–blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the pharmacognostic, phytochemical, biological and spectroscopic analyses of Capparis decidua (Forsk.) Edgew root and stem bark. Methods: Plant material (root and stem bark) was collected, authenticated, shade-dried and extracted by maceration using methanol as a solvent separately. Powder microscopy was performed using a binocular microscope. Fluorescence, physico-chemical analysis and phytochemical screening for the presence of secondary metabolites were performed using standard methods. Brine shrimp lethality bioassay was carried out using Artemia salina bioassay, while enzymatic modulatory study was performed by a-amylase inhibition assay. Microscopic analysis was carried out with scanning electron microscopy. Spectroscopic analysis was performed by Fourier transform infrared spectroscopy (FTIR). Results: Powder microscopy showed the presence of different cellular structures. Various colors were observed under ultraviolet (UV) and ordinary light when treated with different reagents. Phytochemical screening revealed the presence of alkaloids, tannins, saponins and flavonoids but phenol and cardiac glycosides were absent from both extracts. The root bark of the plant showed significant brine shrimp lethality activity. Conclusion: Capparis decidua (Forsk.) Edgew root and stem bark contain a variety of bioactive compounds that have medicinal and therapeutic potentials. Therefore, further investigations are required to elucidate their pharmacological properties. [ABSTRACT FROM AUTHOR]

Zika and dengue virus are flaviviruses which with the passage of time have become a serious challenge affecting millions of people around the world. To lessen the impact of these viral infections globally and to combat these virus-associated epidemics in the future, new medical findings and pharmacological approaches are needed. The phytochemicals extracted from a variety of various plants consist of amazing medicinal properties and can be used in the production of novel anti-viral drugs. The two domains of NS5 protein (NS5 MTase and NS5 RdRp) can be targeted in the clinical trials to produce effective novel inhibitors against Zika and dengue fever. Herein, we aim at using a wide variety of phytochemicals (n = 2035) as inhibitors against NS5 protein from DENV and ZIKV. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the selected compounds were studied to evaluate the pharmacological characteristics. Molecular docking was carried out to determine the binding properties of these ligands with NS5 protein and reactivity was analyzed using molecular orbital energy descriptors. A total of 108 compounds were found suitable in ADMET and from 108 compounds, 35 compounds with the highest in the case of NS5 MTase from ZIKV and DENV were selected. While for NS5 RdRp, 29 compounds were selected. Those compounds, which exhibited remarkable binding affinities values against the proteins of both the ZIKV and 4 serotypes of DENV simultaneously, were predominantly selected in this study. It is concluded that these compounds can be used in clinical trials for the production of a mutual anti-viral drug against both DENV and ZIKV. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains one of the most deadly infections with approximately a quarter of cases not being identified and/or treated mainly due to a lack of resources. Rapid detection of TB or drug-resistant TB enables timely adequate treatment and is a cornerstone of effective TB management. We evaluated the analytical performance of a single-tube assay for multidrug-resistant TB (MDR-TB) on an experimental platform utilising RT-PCR and melting curve analysis that could potentially be operated as a point-of-care (PoC) test in resource-constrained settings with a high burden of TB. Firstly, we developed and evaluated the prototype MDR-TB assay using specimens extracted from well-characterised TB isolates with a variety of distinct rifampicin and isoniazid resistance conferring mutations and nontuberculous Mycobacteria (NTM) strains. Secondly, we validated the experimental platform using 98 clinical sputum samples from pulmonary TB patients collected in high MDR-TB settings. The sensitivity of the platform for TB detection in clinical specimens was 75% for smear-negative and 92.6% for smear-positive sputum samples. The sensitivity of detection for rifampicin and isoniazid resistance was 88.9 and 96.0% and specificity was 87.5 and 100%, respectively. Observed limitations in sensitivity and specificity could be resolved by adjusting the sample preparation methodology and melting curve recognition algorithm. Overall technology could be considered a promising PoC methodology especially in resource-constrained settings based on its combined accuracy, convenience, simplicity, speed, and cost characteristics. [ABSTRACT FROM AUTHOR]

Pyrazinamide is known to be the most effective treatment against tuberculosis disease and is known to have bacteriostatic action. By targeting the bacterial spores, this drug reduces the chances for the progression of the infection in organisms. In recent years, increased instances of the drug resistance of bacterial strains are reported. Pyrazinamidase, activator for pyrazinamide, leads to resistance against the drug due to mutagenicity across the world. The present study aimed at the quantum mechanistic analysis of mutations in pyrazinamidase to gain insights into the mechanism of this enzyme. Quantum mechanical calculations were performed to analyse the effect of mutations at the metal coordination site using ORCA software program. Moreover, conformational changes in PZase binding cavity has also been analysed due to mutations of binding pocket residues using CASTp server. In order to elucidate the behaviour of the mutant pyrazinamidase, docking of PZA in the binding pocket of PZase was performed using AutoDock Vina. Analysis of results revealed that iron showed weak binding with the metal coordination site of the mutant proteins due to alteration in electron transfer mechanism. The binding cavity of the mutant PZase has undergone major conformational changes as the volume of pocket increased due to bulky R-chains of mutated amino acids. These conformational changes lead to weak binding of the drug at binding cavity of PZase and reduce the drug activation mechanism leading to increased drug resistance in the bacterial strains. [ABSTRACT FROM AUTHOR]

Objective Increasing concern over bacterial resistance to cotrimoxazole, which is recommended by WHO as a first-line drug for treating non-severe pneumonia, led to the suggestion that this might not be optimal therapy. However, changing to alternative antimicrobial agents, such as amoxicillin, is costly. We compared the clinical efficacy of twice-daily cotrimoxazole in standard versus double dosage for treating non-severe pneumonia in children. Methods A randomized controlled multicentre trial was implemented in seven hospital outpatient departments and two community health programmes. A total of 1143 children aged 2-59 months with non-severe pneumonia were randomly allocated to receive 4 mg trimethoprim plus 20 mg sulfamethoxazole/kg of body weight or 8 mg trimethoprim plus 40 mg sulfamethoxazole/kg of body weight orally twice-daily for 5 days Treatment failure occurred when a child required a change of therapy, died or was lost to follow-up. Children required a change of therapy if their condition worsened (they developed chest in drawing or danger signs) or if at 48 hours after enrolment, their clinical condition was the same (defined as having a respiratory rate that was 5 breaths/minute higher or lower than at the time of enrolment). Findings The results of 1134 children were analysed: 578 were assigned to the standard dose of cotrimoxazole and 556 to the double dose. Treatment failed in 112 children (19.4%) in the standard group and 118 (21.2%) in the double-dose group (relative risk 1.10; 95% confidence interval = 0.87-1.37). Using multivariate analysis we found that treatment was more likely to fail in children who were not given the medicine correctly ( P = 0.001), in those younger than 12 months ( P = 0.004), those who had used antibiotics previously ( P = 0.002), those whose respiratory rate was ⩾ 20 breaths/minute above the age-specific cut-off point ( P = 0.006), and those from urban areas ( P = 0.042). Conclusion Both standard and double strength cotrimoxazole were equally effective in treating non-severe pneumonia. Close followup of patients is essential to prevent worsening of disease. Definitions of clinical failure need to be more specific. Surveillance in both rural and urban areas is essential in the development of treatment policies that are based on clinical outcomes. [ABSTRACT FROM AUTHOR]

[Display omitted] • We have elaborated the biological perspective of thiazolide derivates against SARS-CoV-2. • 19 derivatives are analyzed opting in silico approaches, targeting MTase and Mpro. • Docking, MD simulations, ADMET studies and DFT analysis are performed. • Through analysis, 4 compounds are screened to show promising results against SARS-CoV-2. • These 4 compounds can be considered candidates for future in vitro and in vivo validations. Humans around the globe have been severely affected by SARS-CoV-2 and no treatment has yet been authorized for the treatment of this severe condition brought by COVID-19. Here, an in silico research was executed to elucidate the inhibitory potential of selected thiazolides derivatives against SARS-CoV-2 Protease (Mpro) and Methyltransferase (MTase). Based on the analysis; 4 compounds were discovered to have efficacious and remarkable results against the proteins of the interest. Primarily, results obtained through this study not only allude these compounds as potential inhibitors but also pave the way for in vivo and in vitro validation of these compounds. [ABSTRACT FROM AUTHOR]

Background: The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking., Objective: Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3., Methods: A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%., Results: After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment., Conclusion: The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at ( https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/ )., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

The recent COVID-19 pandemic has impacted nearly the whole world due to its high morbidity and mortality rate. Thus, scientists around the globe are working to find potent drugs and designing an effective vaccine against COVID-19. Phytochemicals from medicinal plants are known to have a long history for the treatment of various pathogens and infections; thus, keeping this in mind, this study was performed to explore the potential of different phytochemicals as candidate inhibitors of the HR1 domain in SARS-CoV-2 spike protein by using computer-aided drug discovery methods. Initially, the pharmacological assessment was performed to study the drug-likeness properties of the phytochemicals for their safe human administration. Suitable compounds were subjected to molecular docking to screen strongly binding phytochemicals with HR1 while the stability of ligand binding was analyzed using molecular dynamics simulations. Quantum computation-based density functional theory (DFT) analysis was constituted to analyze the reactivity of these compounds with the receptor. Through analysis, 108 phytochemicals passed the pharmacological assessment and upon docking of these 108 phytochemicals, 36 were screened passing a threshold of -8.5 kcal/mol. After analyzing stability and reactivity, 5 phytochemicals, i.e., SilybinC, Isopomiferin, Lycopene, SilydianinB, and Silydianin are identified as novel and potent candidates for the inhibition of HR1 domain in SARS-CoV-2 spike protein. Based on these results, it is concluded that these compounds can play an important role in the design and development of a drug against COVID-19, after an exhaustive in vitro and in vivo examination of these compounds, in future., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Arshia Majeed et al.)

Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.

Background: Chikungunya fever is a challenging threat to human health in various parts of the world nowadays. Many attempts have been made for developing an effective drug against this viral disease and no effective antiviral treatment has been developed to control the spread of the Chikungunya virus (CHIKV) in humans., Objective: This research is aimed at the discovery of potential inhibitors against this virus by employing computational techniques to study the interactions between non-structural proteins of Chikungunya virus and phytochemicals from plants., Methods: Four non-structural proteins were docked with 2035 phytochemicals from various plants. The ligands having binding energies ≥ -8.0 kcal/mol were considered as potential inhibitors for these proteins. ADMET studies were also performed to analyze different pharmacological properties of these docked compounds and to further analyze the reactivity of these phytochemicals against CHIKV, DFT analysis was carried out based on HOMO and LUMO energies., Results: By analyzing the binding energies, Ki, ADMET properties and band energy gaps, it was observed that 13 phytochemicals passed all the criteria to be a potent inhibitor against CHIKV in humans., Conclusion: A total of 13 phytochemicals were identified as potent inhibiting candidates, which can be used against the Chikungunya virus., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming., Objective: Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites., Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing., Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing., Conclusion: It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS., (© 2019 Bentham Science Publishers.)

Protein phosphorylation is one of the most fundamental types of post-translational modifications and it plays a vital role in various cellular processes of eukaryotes. Among three types of phosphorylation i.e. serine, threonine and tyrosine phosphorylation, tyrosine phosphorylation is one of the most frequent and it is important for mediation of signal transduction in eukaryotic cells. Site-directed mutagenesis and mass spectrometry help in the experimental determination of cellular signalling networks, however, these techniques are costly, time taking and labour associated. Thus, efficient and accurate prediction of these sites through computational approaches can be beneficial to reduce cost and time. Here, we present a more accurate and efficient sequence-based computational method for prediction of phosphotyrosine (PhosY) sites by incorporation of statistical moments into PseAAC. The study is carried out based on Chou's 5-step rule, and various position-composition relative features are used to train a neural network for the prediction purpose. Validation of results through Jackknife testing is performed to validate the results of the proposed prediction method. Overall accuracy validated through Jackknife testing was calculated 93.9%. These results suggest that the proposed prediction model can play a fundamental role in the prediction of PhosY sites in an accurate and efficient way.

Dengue fever is one of the most prevalent disease in tropical and sub-tropical regions of the world. According to the World Health Organisation (WHO), approximately 3.5 billion people have been affected with dengue fever. Four serotypes of dengue virus (DENV) i.e. DENV1, DENV2, DENV3 and DENV4 have up to 65% genetic variations among themselves. dengue virus 4 (DENV4) was first reported from Amazonas, Brazil and is spreading perilously due to lack of awareness of preventive measures, as it is the least targeted serotype. In this study, non-structural protein 4B of dengue virus 4 (DENV4-NS4B) is computationally characterised and simulations are performed including solvation, energy minimizations and neutralisation for the refinement of predicted model of the protein. The spiropyrazolopyridone is considered as an effective drug against NS4B of DENV2, therefore, a total of 91 different analogues of spiropyrazolopyridone are used to analyse their inhibitory action against DENV4-NS4B. These compounds are docked at the binding site with various binding affinities, representing their efficacy to block the binding pocket of the protein. Pharmacological and pharmacokinetic assessment performed on these inhibitors shows that these are suitable candidates to be used as a drug against the dengue fever. Among all these 91 compounds, Analogue-I and Analogue-II are analysed to be the most effective inhibitor having potential to be used as drugs against dengue virus.

Severe Combined Immunodeficiency is the term applied to a group of rare genetic disorders characterised by defective or absent T and B cell functions. Patients usually present in first 6 months of life with respiratory/gastrointestinal tract infections and failure to thrive. Among the various types of severe combined immunodeficiency, enzyme deficiencies are relatively less common. We report the case of a 6 years old girl having severe combined immunodeficiency due to adenosine deaminase deficiency.

In the present study two phenacyl derivatives of Harmaline; 2-(7-Methoxy-1-methyl-1,3,4,9-tetrahydro-beta-carbolin-2-yl)-1-(3-nitro-phenyl)-ethanone and 1-(3,4-Dihydroxy-phenyl)-2-(7-methoxy-1-methyl-1,3,4,9-tetrahydro-beta-carbolin-2-yl)-ethanone were synthesized and evaluated for their effect on behaviour of mice, only meta-nitro phenacyl derivative showed activity, which can be compared with parent compound.