Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Safety evaluation of Mikania glomerata and Mikania laevigata in healthy volunteers: A randomized, open label and multiple dose phase I clinical trial

ETHNOPHARMACOLOGICAL RELEVANCE

Mikania glomerata (MG) and Mikania laevigata (ML) leaves are used interchangeably in Brazilian ethno-medicine, mainly for the treatment of respiratory diseases, but there is no clinical trial for the safety evaluation of these species at different doses.

AIM OF THE STUDY

To evaluate the safety of two oral solutions from each species, across two weeks of use and two doses.

MATERIALS AND METHODS

A randomized, open-label, multiple dose, two-arm trial. Participants (n = 19) were randomly allocated to one of the arms of the trial. Group 1 received 15 mL of MG oral solution twice daily for 7 days and then 30 mL twice daily for 7 days. Group 2 received ML oral solution in the same manner. After 14 days, the participants went through a washout period of 7 days before cross-over to the other group. Adverse events (AEs), clinical parameters and blood markers were monitored at the beginning (T0) and at the end of each week of the study, which was concluded after six weeks. AEs were classified as mild, moderate or severe, and according to their correlation with the medicine intake, the measured parameters were compared to T0 by generalized estimating equations (GEE) to determine those affected by the use of the oral solutions (p < 0.05).

RESULTS

MG was not correlated to any of the reported AEs; its use affected three of the evaluated parameters: total protein, diastolic blood pressure and urea, although all the changes were within the normal range for healthy individuals. ML was correlated to one moderate AE of pyrosis and the alteration of 12 parameters: erythrocytes, hemoglobin, hematocrit, monocytes, time of prothrombin, time of thromboplastin, albumin, alkaline phosphatase, total protein, urea, potassium and sodium. All changes were within the normal range for healthy individuals.

CONCLUSION

ML and MG are considered safe for humans under the dose regimen tested.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

Status and Future Scope of Soft Nanoparticles-Based Hydrogel in Wound Healing

Wounds are alterations in skin integrity resulting from any type of trauma. The healing process is complex, involving inflammation and reactive oxygen species formation. Therapeutic approaches for the wound healing process are diverse, associating dressings and topical pharmacological agents with antiseptics, anti-inflammatory, and antibacterial actions. Effective treatment must maintain occlusion and moisture in the wound site, suitable capacity for the absorption of exudates, gas exchange, and the release of bioactives, thus stimulating healing. However, conventional treatments have some limitations regarding the technological properties of formulations, such as sensory characteristics, ease of application, residence time, and low active penetration in the skin. Particularly, the available treatments may have low efficacy, unsatisfactory hemostatic performance, prolonged duration, and adverse effects. In this sense, there is significant growth in research focusing on improving the treatment of wounds. Thus, soft nanoparticles-based hydrogels emerge as promising alternatives to accelerate the healing process due to their improved rheological characteristics, increased occlusion and bioadhesiveness, greater skin permeation, controlled drug release, and a more pleasant sensory aspect in comparison to conventional forms. Soft nanoparticles are based on organic material from a natural or synthetic source and include liposomes, micelles, nanoemulsions, and polymeric nanoparticles. This scoping review describes and discusses the main advantages of soft nanoparticle-based hydrogels in the wound healing process. Herein, a state-of-the-art is presented by addressing general aspects of the healing process, current status and limitations of non-encapsulated drug-based hydrogels, and hydrogels formed by different polymers containing soft nanostructures for wound healing. Collectively, the presence of soft nanoparticles improved the performance of natural and synthetic bioactive compounds in hydrogels employed for wound healing, demonstrating the scientific advances obtained so far.

Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?

OBJECTIVE

This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity.

METHODS

COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve).

RESULTS

The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase.

CONCLUSION

Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.

Chemical composition, cytotoxicity, and antibacterial activity of propolis from Africanized honeybees and three different Meliponini species

ETHNOPHARMACOLOGICAL RELEVANCE

Propolis extracts are widely used in traditional folk medicine and exhibit several properties such as antitumor, anti-inflammatory, and antimicrobial. However, these products have not been investigated in combination with medicines used in clinical practice.

AIM OF THE STUDY

This study aimed to evaluate the chemical composition of propolis extracts from Apis mellifera scutellata and different Meliponini species and characterize their cytotoxicity against tumor cells, antibacterial effects, and interference with the actions of doxorubicin and gentamicin.

MATERIALS AND METHODS

Chromatographic and spectrometric analyses were performed using ultra-high-performance liquid chromatography (UPLC)-tandem mass spectrometry (MS/MS). Propolis extracts were evaluated for cytotoxicity and synergism using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and the antimicrobial activity was examined using the broth microdilution technique and synergism was investigated using checkerboard and time-kill assays.

RESULTS

The chemical characterization revealed the presence of 63 compounds, and the extracts showed selective cytotoxicity against tumor cell lines. Propolis extracts of mandaçaia and mirim exerted selective synergistic cytotoxicity in combination with doxorubicin. Except for the tubuna extract, all evaluated extracts exhibited antibacterial effects on gram-positive strains. Mandaçaia and mirim extracts exerted a synergistic effect with gentamicin; however, only mandaçaia extract exerted a selective effect.

CONCLUSION

Propolis could be a source of antineoplastics and antibiotics. These natural products may reduce the occurrence of doxorubicin and gentamicin related adverse effects, resistance, or both.

A multivariate analysis of risk factors associated with death by Covid-19 in the USA, Italy, Spain, and Germany

Aim

Our aim was to investigate the risk factors associated with death from COVID-19 in four countries: The USA, Italy, Spain, and Germany.

Subject and methods

We used data from the Institute for Health Metrics and Evaluation with projection information from January–August 2020. A multivariate analysis of logistic regression was performed. The following factors were analyzed (per day): number of beds needed for the hospital services, number of intensive care units (ICU) beds required, number of ventilation devices, number of both hospital and ICU admissions due to COVID-19. Nagelkerke’s R² coefficient of determination was used to evaluate the model’s predictive ability. The quality of the model’s fit was assessed by the Hosmer–Lemeshow and the chi-square tests.

Results

Among the evaluated countries, Italy presented greater need for ICU beds/day (≤ 98; OR = 2315.122; CI 95% [334.767–16,503.502]; p < 0.001) and daily ventilation devices (≤ 118; OR = 1784.168; CI 95% [250.217–12,721.995]; p < 0.001). It is expected that both Italy and Spain have a higher ICU admission rate due to COVID-19 (n = 14/day). Spain will need more beds/day (≤ 357; OR = 146.838; CI 95% [113.242–190.402]; p  < 0.001) and probably will have a higher number of daily hospital admissions (n = 48/day). All the above-mentioned factors have an important impact on patients’ mortality due to COVID-19 in all four countries.

Conclusions

Further investments in hospitals’ infrastructure, as well as the development of innovative devices for patient’s ventilation, are paramount to fight the pandemic in the USA, Italy, Spain, and Germany.

Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil

Abstract We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients’ sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted. The median time from symptoms onset to diagnosis was eight days (interquartile range [IQR] 7.23-8.99 days). Half of the patients recovered during the evaluated period, and 8.3% died. Mortality rates were higher in men. Delays in diagnosis were associated with male gender (p = 0.015) and patients living in low SDI areas (p < 0.001). The age groups statistically associated with death were: 70-79 years, 80-89 years, and 90-99 years. Delays to diagnosis greater than eight days were also risk factors for death. Delays in diagnosis and risk factors for death from COVID-19 were associated with male gender, age under 60 years, and patients living in regions with lower SDI. Delays superior to eight days to diagnosis increased mortality rates.

LC-QToF-MS method for quantification of ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma and its application

In this research, we developed and validated a liquid chromatography coupled to mass spectrometry (LC-QToF-MS) method for simultaneous quantification of the anti-tuberculosis drugs ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma. Plasma samples spiked with cimetidine (internal standard) were extracted using protein precipitation with acetonitrile containing 1% formic acid. Separation was performed using a C₁₈ column under flow gradient conditions with water and acetonitrile, both containing 5 mm ammonium formate and 0.1% formic acid. The method was validated according to the ANVISA and US Food and Drug Administration guidelines for bioanalytical method validation. The calibration curve was linear over a concentration range of 0.2-5 μg ml^-1 for ethambutol, 0.2-7.5 μg ml^-1 for isoniazid, 1-40 μg ml^-1 for pyrazinamide and 0.25-2 μg ml^-1 for rifampicin, all with adequate precision and accuracy. The method was reproducible, selective and free of carryover and matrix effects. The validated LC-QToF-MS method was successfully applied to real samples and shown to be applicable to future therapeutic and pharmacokinetic monitoring studies.