Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed ...many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.
Abstract Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed ...approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide ...new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA-small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.
This study aims to investigate the potential analgesic properties of the crude extract of
leaves using in vivo experiments and in silico analysis. The extract, in a dose-dependent manner, exhibited a ...moderate analgesic property (~54% pain inhibition in acetic acid-induced writhing test), which is significant (**
< 0.001) as compared to the control group. The complex inflammatory mechanism involves diverse pathways and they are inter-connected. Therefore, multiple inflammatory modulator proteins were selected as the target for in silico analysis. Computational analysis suggests that all the selected targets had different degrees of interaction with the phytochemicals from the extract. Rutin (RU), protocatechuic acid (PA), vanillic acid (VA), and ferulic acid (FA) could regulate multiple targets with a robust efficiency. None of the compounds showed selectivity to Cyclooxygenase-2 (COX-2). However, regulation of COX and lipoxygenase (LOX) cascade by PA can reduce non-steroidal analgesic drugs (NSAIDs)-related side effects, including asthma. RU showed robust regulation of cytokine-mediated pathways like RAS/MAPK and PI3K/NF-kB by inhibition of EGFR and IKBα (IKK), which may prevent multi-organ failure due to cytokine storm in several microbial infections, for example, SARS-CoV-2. Further investigation, using in vivo and in vitro experiments, can be conducted to develop multi-target anti-inflammatory drugs using the isolated compounds from the extract.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths ...worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.
The novelty of the current article is to investigate the adsorption potential of the Egyptian natural and activated bentonite (Na-bentonite) to inorganic and organic phosphorus (IP, OP) in aqueous ...media. The natural bentonite (NB) was activated to Na-bentonite (Na-B) by a new facile method within 2 h. NB and Na-B were also characterized using XRD, XRF, BET ESM, and FT-IR. The batch experiment has been employed to select the ideal conditions for the removal of inorganic and organic phosphorus (IP, OP) from aqueous solutions. The findings clearly showed that the Na-bentonite is enriched with sodium in the form of Na-montmorillonite with a higher specific area 138.51 m
/g than the value for the natural bentonite 74.21 m
/g. The batch experiment showed maximum absorption for both IP and OP adsorbents occurred at an equilibrium pH = 6, contact time of 40 to 50 min, 40 °C temperature, and a dose rate of 2 mg/L and 1 mg/L, respectively. The equilibrium data displayed better adjustment to Langmuir than the Freundlich, Temkin, and Dubinin-Radushkevich isotherms and adsorption kinetics followed the pseudo-second-order-type kinetic, and the parameters of thermodynamics reveal that adsorption occurs spontaneously and exothermic nature. Na-bentonite proved to be more efficient in removing target material than natural bentonite. The spent bentonites were easily regenerated by chemical methods.
With the significant drawbacks of conventional cancer chemotherapeutics, cancer immunotherapy has demonstrated the ability to eradicate cancer cells and circumvent multidrug resistance (MDR) with ...fewer side effects than traditional cytotoxic therapies. Various immunotherapeutic agents have been investigated for that purpose including checkpoint inhibitors, cytokines, monoclonal antibodies and cancer vaccines. All these agents aid immune cells to recognize and engage tumor cells by acting on tumor-specific pathways, antigens or cellular targets. However, immunotherapeutics are still associated with some concerns such as off-target side effects and poor pharmacokinetics. Nanomedicine may resolve some limitations of current immunotherapeutics such as localizing delivery, controlling release and enhancing the pharmacokinetic profile. Herein, we discuss recent advances of immunotherapeutic agents with respect to their development and biological mechanisms of action, along with the advantages that nanomedicine strategies lend to immunotherapeutics by possibly improving therapeutic outcomes and minimizing side effects.
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Abstract Background Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia caused by insulin resistance (IR) or impaired insulin production. Diabetic retinopathy (DR) ...is a common microvascular disorder which occurs in type 2 diabetes mellitus (T2DM) patients due to chronic hyperglycemia. Previous studies reported that serum zinc (Zn) was associated with certain diabetic microvascular complications. Objective To evaluate relation between serum zinc level and diabetic retinopathy in patients with type 2 diabetes mellitus. Subjects and Methods The current study included 60 subjects with their ages ranging from 18- 60, selected from diabetes and ophthalmology outpatient clinics of Ain Shams University Hospitals for 7 months in a period from July 2022 to January 2023 with matched age and sex . fasting and post prandial blood glucose, hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP),serum creatnine, LDL, HDL, TG,TC and serum zinc levels were measured among groups. Results The study shows statistically significant difference between DM type II subgroups regarding duration of disease (yrs) being higher in DM II with retinopathy 12.5 (10.5 – 16.5) than DM II without retinopathy 9 (6 – 12) with p < 0.001. serum Zn levels were significantly lower in the DR group than the controls and diabetic without retinopathy.in whole DM type II group, there was statistically significant negative correlation found between zinc level and duration of diabetes, HbA1c, LDL, TC, FBS, 2PP and serum creatinine level, while in DM II without retinopathy there was statistically significant negative correlation found between zinc level and duration of diabetes, LDL, TC and FBS and also in DM type II retinopathy there was statistically significant negative correlation found between zinc level and duration of diabetes, HbA1c, FBS, 2PP and serum cretainine level .There is no statistically significant difference found between control group and DM type II group regarding weight, height and BMI of the studied patients with p-value = 0.108, 0.198 and 0.343; respectively. There is no statistically significant difference found between control group and DM type II group regarding systolic and diastolic blood pressure of the studied patients with p- value = 0.554 and 0.405; respectively. Conclusion Our study suggested that lower serum zinc level in T2D patients was related to higher prevalence of diabetic microvascular complications. Patients with lower serum zinc level were more likely to have a longer duration of diabetes, poorer glucose control, and worse β-cell function. Older age, higher HbA1c level, and the prevalence of DN were risk factors related to the lower serum zinc level.
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