COVID-19 Drug Research Articles

Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.

Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.

Holistic AI-Based Prediction Model for COVID-19 Drug Efficacy in Patients with Comorbidities

Holistic AI-Based Prediction Model for COVID-19 Drug Efficacy in Patients with Comorbidities

Occurrence of selected Covid-19 drugs in surface water resources: a review of their sources, pathways, receptors, fate, ecotoxicity, and possible interactions with heavy metals in aquatic ecosystems

The outbreak of the coronavirus disease 2019 (Covid-19) led to the high consumption of antibiotics such as azithromycin as well as corticosteroids such as prednisone, prednisolone, and dexamethasone used to treat the disease. Seemingly, the concentrations of these four Covid-19 drugs increased in wastewater effluents and surface water resources. This is due to the failure of traditional wastewater treatment facilities (WWTFs) to eliminate pharmaceuticals from wastewater. Therefore, the objective of the current research was to review the present state of literature on the occurrence of four Covid-19 drugs in water resources, the associated risks and toxicity, their fate, as well as the emergence of combined pollutants of Covid-19 drugs and heavy metals. From late 2019 to date, azithromycin was observed at concentrations of 935 ng/L, prednisone at 433 ng/L, prednisolone at 0.66 ng/L, and dexamethasone at 360 ng/L, respectively, in surface water resources. These concentrations had increased substantially in water resources and were all attributed to pollution by wastewater effluents and the rise in Covid-?19 infections. This phenomenon was also exacerbated by the observation of the pseudo-persistence of Covid-19 drugs, long half-life periods, as well as the excretion of Covid-19 drugs from the human body with about 30?90% of the parent drug. Nonetheless, the aquatic and human health toxicity and risks of Covid-19 drugs in water resources are unknown as the concentrations are deemed too low; thus, neglecting the possible long-term effects. Also, the accumulation of Covid-19 drugs in water resources presents the possible development of combined pollutants of Covid-19 drugs and heavy metals that are yet to be investigated. The risks and toxicity of the combined pollutants, including the fate of the Covid-19 drugs in water resources remains a research gap that undoubtably needs to be investigated.

Therapeutic Potential of Neutralizing Monoclonal Antibodies (nMAbs) against SARS-CoV-2 Omicron Variant.

The COVID-19 pandemic has spurred significant endeavors to devise treatments to combat SARS-CoV-2. A limited array of small-molecule antiviral drugs, specifically monoclonal antibodies and interferon therapy, have been sanctioned to treat COVID-19. These treatments typically necessitate administration within ten days of symptom onset. There have been reported reductions in the effectiveness of these medications due to mutations in non-structural protein genes, particularly against Omicron subvariants. This underscores the pressing requirement for healthcare systems to continually monitor pathogen variability and its impact on the efficacy of prevention and treatments. This review aimed to comprehend the therapeutic benefits and recent progress of nMAbs for preventing and treating the Omicron variant of SARS-CoV-2. Neutralizing monoclonal antibodies (nMAbs) provide a treatment avenue for severely affected individuals, especially those at high risk for whom vaccination is not viable. With their specific epitope affinity, they pose no significant risk of severe adverse effects. The degree of reduction in neutralization varies significantly across different monoclonal antibodies and variant combinations. For instance, Sotrovimab maintained its neutralization effectiveness against Omicron BA.1, but exhibited diminished efficacy against BA.2, BA.4, BA.5, and BA.2.12.1. Bebtelovimab has been observed to preserve its efficacy against all subtypes of the Omicron variant. Subsequently, WKS13, mAb-39, 19n01, F61-d2 cocktail, etc., have become effective. This review has highlighted the therapeutic implications of nMAbs in SARS-CoV-2 Omicron treatment and the progress of COVID-19 drug discovery.

Comprehensive In silico and In vitro evaluation of Plectranthus amboinicus essential oil: A promising inhibitor of SARS-CoV-2 B.1.1.529 Omicron variant, proinflammatory biomarkers and diabetes-related enzymes

Existing challenges, such as the viral SARS-CoV-2 B.1.1.529 Omicron variants, adverse reactions, and limited availability of drugs for COVID-19 continue to challenge global health. Severe inflammatory response associated with COVID-19 has been shown to worsen diabetic conditions and finding a range of treatment options for COVID-19 is significant. The research investigated the potential of Plectranthus amboinicus (Lour.) Spreng. (Lamiaceae) essential oil (PEO), known for its bioactive terpene content, as a multifaceted therapeutic agent. In silico and in vitro approaches were conducted to evaluate its inhibitory effects on the replication of Omicron variant, its ability to modulate proinflammatory biomarkers, and its impact on diabetes-related enzymes. PEO was extracted by hydrodistillation method. GC-MS analysis revealed 62 compounds. In silico molecular docking showed that the top ten PEO major components exhibited strong binding affinities with Omicron 3CL protease (-4.0 to −6.1 kcal/mol) and spike-RBD protein (-6.1 to −9.2 kcal/mol). PEO (6.25–200 μg mL⁻¹) significantly inhibited the B.1.1.529 Omicron SARS-CoV-2 3CL protease and Spike-RBD. PEO showed a cytotoxic effect at >80 μg mL⁻¹ in LPS-induced macrophage and inhibited production of TNF-α from 6.25 to 50 μg mL⁻¹, indicating its potential to mitigate the cytokine storm in link with severe COVID-19 cases. In vitro, PEO at 6.25–200 μg mL⁻¹ significantly inhibited α-amylase and α-glucosidase enzymes, suggesting that PEO components could modulate key enzymes involved in glucose metabolisms. These findings showed the potential of PEO as an antiviral, anti-inflammatory, and antidiabetic agent and offer an opportunity to further clinical investigations in testing the efficacy as an alternative approach to managing COVID-19 and its complications.

Comparative Study of Docking Tools for Evaluation of Potential Copper Metallodrugs and Their Interaction with TMPRSS2

COVID-19 has caused over seven million deaths globally due to its high transmission rate. The virus responsible for the disease requires a transmembrane protease serine type II (TMPRSS2-7MEQ) to infiltrate host cells and has been linked to several cancers, particularly prostate cancer. To investigate COVID-19 potential therapies, a series of Casiopeina-like copper complexes containing 1,10-Phenanthroline and amino acids were investigated as TMPRSS2 inhibitors. The molecular structures of twelve Phenanthroline copper complexes were calculated, and their global reactivity indices were analyzed using DFT and conceptual DFT methods. Three molecular docking algorithms were employed to identify the most effective inhibitors by examining their interactions with amino acid residues in the target protein’s catalytic activity triad (Asp345, His296, and Ser441). All complexes are docked above the catalytic site, blocking the interaction with substrates. The Phenanthroline complexes showed better interactions than the Bipyridine complexes, likely due to increased hydrophobic contacts. Analogs’ cationic nature and amino acids’ basic side chains bring them near the active site by interacting with Asp435. The top complexes in this study contain Ornithine, Lysine, and Arginine, making them promising alternatives for researching new drugs for COVID-19 and cancers like prostate cancer.

Real-world analysis of safety, tolerability, and adherence to nirmatrelvir-ritonavir (paxlovid) in primary care COVID-19 outpatients.

This retrospective cross-sectional study aims to evaluate the safety, tolerability, and adherence of patients prescribed Nirmatrelvir-ritonavir (Paxlovid) in outpatient settings, focusing on its use in managing category 2 COVID-19 patients across three primary healthcare clinics in Selangor, Malaysia. Data were collected from the Paxlovid pharmacy registry and medical records at Klinik Kesihatan Seksyen 7, Klinik Kesihatan Seksyen 19, and Klinik Kesihatan Kelana Jaya between April 1, 2022, and November 30, 2022. This study analysed data from 415 category 2 COVID-19 patients aged ≥ 18years. The primary and secondary outcomes included the assessment of patient demographics, Paxlovid dosing, current medication, changes in drug regimen, adherence, and adverse drug reactions (ADR). Pharmacists follow-ups were conducted on days 3 and 5 post-medication initiation. The majority (79.5%) of the cohort experienced ADR, predominantly dysgeusia, diarrhoea, body ache, vomiting, and nausea. Despite this, the ADRs were generally well-tolerated, with no severe impacts reported. High adherence was observed, with 96.9% of patients completing the 5-day regimen. The primary reasons for non-adherence included adverse effect intolerability, dosing ambiguity, forgetfulness, concerns about ADR, and perceived health improvement. Notable medications interacting with Paxlovid were simvastatin, amlodipine, and atorvastatin, and 21.7% of 23 concurrent medications were found not to comply with the recommended interventions by the University of Liverpool COVID-19 Drug Interaction database. Paxlovid demonstrates a high level of safety and tolerability in outpatient COVID-19 patients, with optimal adherence observed. This study underscores the vital role of healthcare professionals in managing Paxlovid within primary healthcare and highlights the need for broader research and direct patient involvement to enhance treatment strategies against COVID-19.

Hospital-Level Variation in COVID-19 Treatment Among Hospitalized Adults in the United States: A Retrospective Cohort Study.

Retrospective cohort study. To characterize variation in dexamethasone and remdesivir use over time among hospitals. Little is known about hospital-level variation in COVID-19 drug treatments in a large and diverse network in the United States. We selected individuals hospitalized with COVID-19 across 163 hospitals between February 23, 2020 and October 31, 2021 from using the HCA CHARGE, an electronic health record repository from a network of community health care facilities in the United States. We quantified receipt of dexamethasone, remdesivir, and combined use of dexamethasone and remdesivir during the hospital stay. We used 2-level logistic regression models to determine the intraclass correlation coefficient (ICC) at the hospital level, adjusting for patient and hospital characteristics. The ICC shows the proportion of total variation in drug use accounted for by hospitals. Among 161,667 individuals hospitalized with COVID-19, 73.0% were treated with dexamethasone, 49.1% with remdesivir, and 45.0% with both dexamethasone and remdesivir. The proportion of variation in dexamethasone use was 12.7% (adjusted ICC: 0.127), 8.5% for remdesivir, and 11.3% for combined drug use, indicating low interhospital variation. In the fully adjusted models, between-facility variation in dexamethasone use declined from 34.1% in February-March 2020 to 11.3% in January-March 2021 and then increased to 17.3% in July-October 2021. The variation in remdesivir use remained relatively stable during the study period. During the first 2 years of the pandemic, there was relatively consistent use of dexamethasone and remdesivir across the hospitals examined. Consistent adoption and implementation of treatment guidelines across the hospitals examined may have led to a decrease in variation in drug usage over time.

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes. This strategy holds significant promise for newly emerging diseases, as drug discovery is a lengthy and expensive process. Through this study, we present an ensemble method based on the convolutional neural network integrated with genetic algorithm and deep forest classifier for virus-drug association prediction (CGDVDA). We generated feature vectors by combining drug chemical structure and virus genomic sequence-based similarities, and extracted prominent deep features by applying the convolutional neural network. The convoluted features are optimized using the genetic algorithm and classified using the ensemble deep forest classifier to predict novel virus-drug associations. The proposed method predicts drugs for COVID-19 and other viral diseases in the dataset. The model could achieve ROC-AUC scores of 0.9159 on fivefold cross-validation. We compared the performance of the model with state-of-the-art approaches and classifiers. The experimental results and case studies illustrate the efficacy of CGDVDA in predicting drugs against viral infectious diseases.

Phytoconstituents of Citrus limon (Lemon) as Potential Inhibitors Against Multi Targets of SARS-CoV-2 by Use of Molecular Modelling and In Vitro Determination Approaches.

In the present work, phytoconstituents from Citrus limon are computationally tested against SARS-CoV-2 target protein such as Mpro - (5R82.pdb), Spike - (6YZ5.pdb) &RdRp - (7BTF.pdb) for COVID-19. Docking was done by glide model, QikProp was performed by in silico ADMET screening & Prime MM-GB/SA modules were used to define binding energy. When compared with approved COVID-19 drugs such as Remdesivir, Ritonavir, Lopinavir, and Hydroxychloroquine, plant-based constituents such as Quercetin, Rutoside, Naringin, Eriocitrin, and Hesperidin. bind with significant G-scores to the active SARS-CoV-2 place. The constituents Rutoside and Eriocitrin were studied in each MD simulation in 100 ns against 3 proteins 5R82.pdb, 6YZ5.pdb and 7BTF.pdb.We performed an assay with significant natural compounds from contacts and in silico results (Rutin, Eriocitrin, Naringin, Hesperidin) using 3CL protease assay kit (B.11529 Omicron variant). This kit contained 3CL inhibitor GC376 as Control. The IC50 value of the test compound was found to be Rutin -17.50 μM, Eriocitrin-37.91 μM, Naringin-39.58 μM, Hesperidine-140.20 μM, the standard inhibitory concentration of GC376 was 38.64 μM. The phytoconstituents showed important interactions with SARS-CoV-2 targets, and potential modifications could be beneficial for future development.

Crystal structures, NCI-RDG, Hirshfeld surface and energy framework analysis of tetra aryl substitute bispidine and oxaquinuclidine core containing derivatives: A potential COVID-19 drug candidate

Two of the title compounds are crystallized in P21/c and monoclinic face groups. Each crystal unit cell has four molecules. There are two piperidine rings: one in the chair conformation and another one in the boat conformation. The piperidine nitrogen in the boat conformation is joined to the oxygen-containing chair conformation piperidine ring to produce the oxaquinuclidine ring (-N-CH2O-). Furthermore, because of the steric barrier between rings, all aryl groups are oriented in an equatorial position. The crystal network is maintained through several interactions, including C—H⋯O/N/Cl/π. The crystal void and interaction energy studies gave additional evidence of the strength of the crystal packing and intermolecular linkage. Crystal 3b has less cavity than crystal 3c, indicating that it is densely packed. The dispersion energy contribution dominates the stability, according to the analysis of the electrostatic, dispersion, and total energy frameworks. Reduced density gradient (RDG), electron localization function (ELF), and localized orbital locator (LOL) analysis were used to determine the molecular interaction, electron pair density, and maximally localized orbitals overlapping sites, respectively. This approach reveals that oxaquinuclidine ring steric hindrance and the electron densities throughout the molecules. Density function theory (DFT) provides more evidence for the ring's orientation and the molecules' reactivity. A molecular docking investigation of four essential COVID-19 proteins was carried out using these theoretical ideas. This investigation demonstrates that each protein's reactivity varies; certain proteins have more interaction with high electron density molecules (electron-donating substituent), while others have more interaction with low electron density molecules (electron-withdrawing substituent). The ADMET and drug-likeness investigations reveal that methoxy group substituted molecules perfectly correlate with the required pharmacological characteristics. The cytotoxicity of the five compounds was assessed using the HEK293T cell.

Evaluation of Drug and Herbal Medicinal Promotions on Social Media During the COVID-19 Pandemic in Relation to World Health Organization Ethical Criteria and South African Health Products Regulatory Authority Guidelines in South Africa: Cross-Sectional Content Analysis.

Consideration of ethics in the promotion of medications is essential to safeguard the health of consumers, particularly during health crises. The World Health Organization (WHO) and the South African Health Products Regulatory Authority (SAHPRA) have established stringent standards to ensure the integrity of pharmaceutical promotions and safeguard public health, including advertisements on the internet and social media platforms. However, the dynamic nature of online advertising poses challenges for monitoring and enforcing ethical standards. The study aimed (1) to examine the COVID-19 drug and medicinal promotions across online platforms and social media from 2020 to 2022 in South Africa and (2) to ensure that drug promotions adhere to ethical guidelines outlined by the WHO and SAPHRA. A cross-sectional content analysis was conducted to assess drug and medicinal advertisements across various internet and social media platforms. A systematic approach was used to identify and analyze promotional content, focusing on adherence to ethical guidelines outlined by WHO and SAPHRA. Data were collected and analyzed to determine the extent of compliance and identify any potential violations or areas for improvement. A total of 14 online drug advertisements were included in this analysis. Our findings show that most of the drugs advertised did not meet the regulations and guidelines provided by WHO and SAHPRA. There were omissions about active ingredients, proprietary names, adverse drug responses, precautions, and overdosage and adverse drug reactions. Traditional medicines were not fully consistent with the approved WHO ethical criteria data sheet. Our analysis highlights the critical importance of ensuring compliance with ethical guidelines in drug promotions on the internet and social media platforms. There is a need for continued vigilance and enforcement efforts to uphold ethical standards and protect the health of the public. Ongoing monitoring and collaboration between national drug regulatory agencies, pharmaceutical companies, and online platforms will be essential for promoting responsible advertising. In addition, safety monitoring and pharmacovigilance systems for herbal medicinal products are yet to be established.

Anti- COVID-19 drug discovery by flavonoid derivatives: an extensive computational drug design approach.

The present study deals with the in-silico analyses of several flavonoid derivatives to explore COVID-19 through pharmacophore modelling, molecular docking, molecular dynamics, drug-likeness, and ADME properties. The initial literature study revealed that many flavonoids, including luteolin, quercetin, kaempferol, and baicalin may be useful against SARS β-coronaviruses, prompting the selection of their potential derivatives to investigate their abilities as inhibitors of COVID-19. The findings were streamlined using in silico molecular docking, which revealed promising energy-binding interactions between all flavonoid derivatives and the targeted protein. Notably, compounds 8, 9, 13, and 15 demonstrated higher potency against the coronavirus Mpro protein (PDB ID 6M2N). Compound 8 has a -7.2 Kcal/mol affinity for the protein and binds to it by hydrogen bonding with Gln192 and π-sulfur bonding with Met-165. Compound 9 exhibited a significant interaction with the main protease, demonstrating an affinity of -7.9 kcal/mol. Gln-192, Glu-189, Pro-168, and His-41 were the principle amino acid residues involved in this interaction. The docking score for compound 13 is -7.5 Kcal/mol, and it binds to the protease enzyme by making interactions with Leu-41, π-sigma, and Gln-189. These interactions include hydrogen bonding and π-sulfur. The major protease and compound 15 were found to bind with a favourable affinity of -6.8 Kcal/mol. This finding was further validated through molecular dynamic simulation for 1ns, analysing parameters such as RMSD, RMSF, and RoG profiles. The RoG values for all four of the compounds varied significantly (35.2-36.4). The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADME and drug-likeness properties, fulfilling all the necessary criteria. The findings of the study may support further efforts for the discovery and development of safe drugs to treat COVID-19.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders.

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

COVID-19 pandemic-related drugs and microplastics from mask fibers jointly affect soil functions and processes

The COVID-19 pandemic has led to an unprecedented increase in pharmaceutical drug consumption and plastic waste disposal from personal protective equipment. Most drugs consumed during the COVID-19 pandemic were used to treat other human and animal diseases. Hence, their nearly ubiquitous presence in the soil and the sharp increase in the last 3 years led us to investigate their potential impact on the environment. Similarly, the compulsory use of face masks has led to an enormous amount of plastic waste. Our study aims to investigate the combined effects of COVID-19 drugs and microplastics from FFP2 face masks on important soil processes using soil microcosm experiments. We used three null models (additive, multiplicative, and dominative models) to indicate potential interactions among different pharmaceutical drugs and mask MP. We found that the multiple-factor treatments tend to affect soil respiration and FDA hydrolysis more strongly than the individual treatments. We also found that mask microplastics when combined with pharmaceuticals caused greater negative effects on soil. Additionally, null model predictions show that combinations of high concentrations of pharmaceuticals and mask MP have antagonistic interactions on soil enzyme activities, while the joint effects of low concentrations of pharmaceuticals (with or without MP) on soil enzyme activities are mostly explained by null model predictions. Our study underscores the need for more attention on the environmental side effects of pharmaceutical contamination and their potential interactions with other anthropogenic global change factors.

Phenytoin from antiepileptic to covid-19: Synthesis, crystal structure, DFT, HSA, MEP and green biological study of phenytoin derivative as potential covid-19 drug candidates

That's an interesting point! Phenytoin's potential in reducing cognitive deficits during COVID-19 is indeed intriguing. Its established status as an approved antiepileptic medication could lend credibility to exploring its effects on cognitive function in COVID-19 patients. This dual functionality could provide a unique angle for research and potential therapeutic interventions. This study reports the synthesis, characterization and importance of a new phenytoin derivative PD, C23H24N4O5. A biological molecular modelling study using a computer-aided tool, which is a clean technique of PD compared to the approved Phenytoin anti-COVID-19 drugs, was achieved. Docking the three compounds into 7JQ0 of COVID-19 Mpro showed the potential capability of PD compared to Phenytoin and the inhibitor. Binding affinity, for example, was -7.3, -6.9 and -6.6 kcal/mol, respectively. Noticeably, PD interacted with 75 % of the essential functional Mpro amino acids, such as G143 and H164, M49, C145, M165 and E166, comparable to the standards used. Further interesting findings that predicted compound toxicity and drug-likeness also found that PD was less toxic and bio-available as a drug than Phenytoin and the inhibitor. Superposition of our ligand PD against Phenytoin and the Inhibitor docked into7JQ0 using the same parameters showed similar interactions in the amino acid cavity, enhancing the possibility of PD being an anti-COVID-19 drug. The free computer-aided methods were used to significantly speed up the initial stages of drug discovery by narrowing down potential candidates for further laboratory testing.This compound adopts an L-shaped conformation. The lone pairs on the ring nitrogen atoms participate in π bonding in the ring. In the crystal, a layer structure was generated by NH···N, NH···O and CH···O hydrogen bonds plus CH···π(ring) interactions. Theoretical studies of the compound were calculated using the density functional theory (DFT) method with a B3LYP 6–311++G(d,p) basis set. The geometrical parameters after optimization were compared with the experimental values. Molecular Electrostatic Potential (MEP) and Hirshfeld Surface Analysis (HSA) were studied to investigate intermolecular interactions. Frontier Molecular Orbitals (FMOs), which were Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO), were created, and the energy gap between these orbitals was calculated to understand the chemical stability of the molecule.

COVID-19 in patients with rheumatic diseases: frequency and course

Objectives. The aim of the study was to assess frequency and severity of COVID-19 in patients with rheumatic diseases (RD).Patients and methods. The study included information on the presence or absence of COVID-19 in the medical history of 9185 patients with immunoinflammatory RD (IIRD) and 491 patients with osteoarthritis (OA) who were observed at the V. A. Nasonova Research Institute of Rheumatology from September 21, 2021 to December 28, 2023.Results. The incidence of COVID-19 in the analyzed IIRD was significantly higher compared to OA (p<0.02). All IIRD included in the analysis are characterized by an increased risk of COVID-19 incidence when compared with OA by 1.7–3.5 times. Patients with rheumatoid arthritis, ankylosing spondylitis, Sjögren's disease, systemic sclerosis, psoriatic arthritis, systemic lupus erythematosus, microcrystalline arthritis, ANCA- associated vasculitis and poly/dermatomyositis were significantly more likely (p<0.009) to receive COVID-19 therapy compared with the control group. Patients with these diseases are characterized by an increased risk of treatment for COVID-19 by 1.7–6.5 times compared with OA. Also, patients with inflammatory joint diseases (IJD), connective tissue diseases (CTDs) and systemic vasculitis (SV) were hospitalized with COVID-19 more often than patients with OA (p=0.013, p=0.003 and p<0.001, respectively). Patients with IJD, CTDs and SV are characterized by an increased risk of hospitalization with COVID-19 by 3.5–6.8 times compared with OA. In addition, elderly patients with IIRD are characterized by an increasing risk of treatment, hospitalization and use of biologics or targeted synthetic disease-modifying drugs for COVID-19.Conclusion. According to the data obtained, the problem of COVID-19 remains very significant for patients with RD. This dictates the need to continue studying risk factors for adverse outcomes of the disease and vaccine prevention of this infectious pathology.

Peranan Kortikosteroid Terhadap Luaran Pasien COVID-19

Corticosteroid is one of the COVID-19 drug agents that has a pros and cons of its use because there has been a research that shows this drug had a negative impact and also there has been a research that recommend its use.This literature study conducted to discuss about role of corticosteroid towards COVID-19 patient’s outcome. This paper is a literature review that evaluates variant journals of original research with clinical study design about role of corticosteroid towards COVID-19 patient's outcome by selecting through electronic database such as PubMed, ScienceDirect, and CENTRAL with designated keywords. There are 16 literatures of clinical study design found through PubMed, ScienceDirect, and CENTRAL. It is know that the role of corticosteroid depend on patient's clinical characteristic and time given to the patients. It can be concluded that corticosteroid had role on improving oxygenation on patients with moderate-critical symptoms, reducing mortality rate on critical patients with hyperinflammation, and reducing the lenght of stay in the hospital.

Child COVID-19 vaccine uptake among participants of the 2015 Pelotas (Brazil) Birth Cohort Study

ObjectiveTo analyze COVID-19 vaccine uptake in children and to investigate factors associated with two outcomes variables: (a) not even beginning; (b) not completing the COVID-19 vaccine series. MethodsWe used data of children aged 6–7 years from the 2015 Pelotas c Birth Cohort Study. COVID-19 vaccination status was collected from immunization cards and National Immunization Program Information System. Adjusted analyses were performed using a hierarchical model to identify factors associated with the two study outcomes. ResultsAmong 3867 children, 20.7 % (95 % CI, 19.5 %–22.0 %) did not even begin the 2-dose primary COVID-19 vaccine series, and 28.2 % (95 % CI, 26.6 %–29.8 %) did not complete the series with the second dose. Children not even beginning the COVID-19 vaccine series were more likely to have a White mother, not to have obesity, to have a history of COVID-19 infection, to have received non-recommended drugs for COVID-19, to be afraid of needles, and to have an incomplete diphtheria-tetanus-pertussis (DTP) and poliovirus immunization schedule. Not completing the 2-dose series was associated with lower maternal age and education, mother’s self-identification as White or Brown, lower household income, lack of access to health services, not having completed the DTP and poliovirus immunization schedule and living with a person with a history of infection with COVID-19. ConclusionThe results highlight a vaccine-hesitant parents’ group who chose not beginning the COVID-19 vaccine series of their children and, another group of parents who failure to complete the child’s series due to difficulty accessing health services.

Rapid and ecofriendly ultra performance liquid chromatographic analytical methodology for the simultaneous determination of four drugs included in COVID-19 treatment protocol

The global spread of COVID-19 disease has increased the demand for new therapeutic approaches. The clinical studies revealed that remdesivir and molnupiravir have been effective against COVID-19. Remdesivir (REM) is the first FDA-approved antiviral drug for COVID-19, while molnupiravir is the first oral antiviral drug for SARS-CoV-2 infections. In addition to antivirals, anticoagulants such as apixaban (APX) and antibiotics such as levofloxacin (LVF) are also included in the treatment protocols. This study suggests and validates a rapid and selective method for the determination of the four drugs involved in the COVID-19 treatment protocol. The simultaneous separation and determination of REM, MLP, LVF, and APX in human plasma were achieved using UPLC with a fluorophenyl column and a mobile phase composed of acetonitrile and water acidified with formic acid (pH = 3) in the ratio of 45: 55, v/v, at a flow rate of 0.1 mL/min. The PDA detector was adjusted at 254 nm. The method could simultaneously determine the four drugs in a single run in less than 6 min. The method exhibited linearity over the concentration ranges of 1.0–15.0 µg/mL, 0.1–5.0 µg/mL, 0.1–2.0 µg /mL, and 0.1–5.0 µg/mL, for LVF, MLP, APX and REM, respectively. The proposed method was validated following the International Council on Harmonization Q2(R1) guidelines and the findings were compared statistically to previously reported methods. The recovery rates of the drugs from spiked human plasma were satisfactory. The greenness of the suggested method was evaluated using three metrics, namely, Analytical Eco-scale, Green Analytical Procedure Index (GAPI) metric, and the Analytical GREEnness measure approach (AGREE).