COVID-19 is an emergency public health problem of global importance. This study aimed to investigate the effect of foods and nutrients as complementary approaches on the recovery from COVID-19 in 170 ...countries, especially considering the complexity of the disease and the current scarcity of active treatments.
A retrospective study was performed using the Kaggle database, which links the consumption of various foods with recovery from COVID-19 in 170 countries, using multivariate analysis based on a generalized linear model.
The results showed that certain foods had a positive effect on recovery from COVID-19: eggs, fish and seafood, fruits, meat, milk, starchy roots, stimulants, vegetable products, nuts, vegetable oil and vegetables. In general, consumption of higher levels of proteins and lipids had a positive effect on COVID-19 recovery, whereas high consumption of alcoholic beverages had a negative effect. In developed countries, where hunger had been eradicated, the effect of food on recovery from COVID-19 had a greater magnitude than in countries with a higher global hunger index (GHI), where there was almost no identifiable effect.
Several foods had a positive effect on COVID-19 recovery in developed countries, especially food groups with a higher content of lipids, proteins, antioxidants and micronutrients (e.g., selenium and zinc). In countries with extreme poverty (high GHI), foods presented little effect on recovery from COVID-19.
Role of Acetic Acid Bacteria in Food and Beverages Yassunaka Hata, Natália Norika; Surek, Monica; Sartori, Daniele ...
Food technology and biotechnology,
01/2023, Letnik:
61, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Acetic acid bacteria (AAB) are microorganisms widely distributed in nature. Although this group is involved in the spoilage of some foods, AAB are of great industrial interest, and their ...functionality is still poorly understood. AAB convert ethanol, sugars and polyols into various organic acids, aldehydes and ketones
oxidative fermentation. These metabolites are produced during a succession of biochemical reactions in various fermented foods and beverages, such as vinegar, kombucha, water kefir, lambic and cocoa. Furthermore, important products such as gluconic acid and ascorbic acid precursors can be produced industrially from their metabolism. The development of new AAB-fermented fruit drinks with healthy and functional properties is an interesting niche for research and the food industry to explore, as it can meet the needs of a wide range of consumers. Exopolysaccharides such as levan and bacterial cellulose have unique properties, but they need to be produced on a larger scale to expand their applications in this area. This work emphasizes the importance and applications of AAB during the fermentation of various foods, their role in the development of new beverages as well as numerous applications of levan and bacterial cellulose.
Clove leaf essential oil (CEO) is renowned for its well-established bioactive properties, which makes it an element with high added-value, especially for the food and pharmaceutical industries. ...Nevertheless, its use is hindered by instability and limited water solubility. Therefore, this study aimed to overcome these challenges by developing a stable Pickering emulsion of clove leaf essential oil using octenyl succinic anhydride modified waxy maize starch (OSA starch) particles as stabilizing agent, providing an eco-friendly alternative to conventional surfactants. First, the experimental conditions for the development of two distinct emulsion were optimized using a Response Surface Methodology (RSM). As a result, slightly shear-thickening behavior emulsions were obtained, with droplet size from 1 to 6 μm, absolute zeta potential values above 30 mV, and an exceptional stability for more than a month. These aspects can be linked to the synergistic effect of chemical/electrostatic interaction between essential oil and OSA starch. Finally, both samples exhibited an outstanding antibacterial efficacy, with MBC/MIC values below 4, classifying them as bactericidal agents against some typical foodborne pathogens. Our results indicate that these Pickering emulsions have noteworthy properties that make them an additive with promising potential for application in the food industry.
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•Pickering emulsion of clove leaf essential oil was formed with modified starch.•The experimental conditions were optimized with Response Surface Methodology (RSM).•An emulsion showed 100 % stability after 44 days of storage.•Pickering emulsions exhibited shear-thickening behavior.•Antibacterial activity of emulsions was similar to the standard antibiotic.
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•Occurrence of antibiotics and antibiotic-resistant bacteria in Brazilian rivers.•Strains ESBL and AmpC were found in the water of the urban river.•Some isolates of Enterococcus spp. ...were resistant to vancomycin and gentamicin.•Contamination in rivers may be contributing to the spread of bacterial resistance.
The occurrence of antibiotics in the natural environment has been a growing issue and correlations between this presence and developing resistance bacteria are explored. The purpose of this study was to investigate the presence of antibiotics of different classes and associated resistant bacteria, in water samples taken from urban river waters in Curitiba, Brazil. A method for the quantification of antibiotics (azithromycin, amoxicillin, norfloxacin ciprofloxacin, doxycycline and sulfamethoxazole) was developed and validated using liquid chromatography coupled with mass spectrometry. To investigate and identify coliforms resistant to these antibiotics, we performed selective microbiological culturing techniques. We detected antibiotics in our water samples; concentrations ranged from 0.13 to 4.63 μg L−1, with the highest being amoxicillin at 4.63 μg L-1. In all water samples this study, antibiotic resistant bacteria were detected. Escherichia coli was resistant to amoxicillin, norfloxacin, ciprofloxacin, doxycycline and sulfamethoxazole. Strains producing β-lactamase with extended spectrum (ESBL and AmpC) were also found in these isolates. Enterococcus spp. displayed resistance to norfloxacin and ciprofloxacin, and some isolates were resistant to vancomycin, gentamicin and streptomycin (complementary tests). No P. aeruginosa resistant strains were observed. It is possible these antibiotics came from domestic effluents and may be contributing to the spread of bacterial resistance
This study aimed to implement and evaluate machine learning based-models to predict COVID-19’ diagnosis and disease severity.
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).
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.
Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.
•A new method to predict the diagnosis and severity of COVID-19 was developed.•Biochemical tests and machine learning were able to predict positivity and disease severity.•The accuracy of the diagnostic and severity models was greater than 84%.•The artificial neural network model performed best in both predictions.•Ferritin was the most important biomarker in predicting diagnosis and severity.
Introduction
We aimed to develop and validate machine learning (ML) -based algorithms to predict COVID-19 diagnosis as well as to identify new biomarkers associated with the disease.
Methods
...Initially, 96 blood samples of patients diagnosed with COVID-19 (Thaizhou Hospital, China) were analyzed through liquid chromatography coupled to mass spectrometry. Samples of patients presenting other pneumonias or severe acute respiratory syndrome, but with negative RT-PCR for SARS-CoV-2, were used as positive controls. Samples from healthy volunteers were used as negative controls. The final database included around 1000 metabolites. Exploratory analyses for the development of ML-based models using principal component analysis (PCA) were performed. Leverage plot versus studentized residuals method was used to detect outliers. Three supervised ML-based models were developed: discriminant analysis by partial least squares (PLS-DA), artificial neural networks discriminant analysis (ANNDA) and k-nearest neighbors (KNN). Samples for the training (70%) and testing sets (30%) were randomly selected using the Kenrad Stone algorithm. Models’ performance was evaluated considering accuracy, sensitivity and specificity. Analyses were conducted in SOLO (Eigenvector-Research).
Results
The PCA model was able to distinguish the three classes of patients’ samples (positive for COVID-19, negative controls, positive controls) with an overall accumulated variance of 94.27 percent. The PLS-DA model presented the best performance (accuracy, sensitivity, and specificity of 93%, 98% and 88%, respectively). Increased levels of the biomarkers uridine (linked to glucose homeostasis, lipid, and amino acid metabolisms), 4-hydroxyphenylacetoylcarnitine (metabolite from the tyrosine metabolism; probably associated with anorexia) and ribothymidine (resulting from oral and fecal microbiota alterations) were significantly associated with COVID-19.
Conclusions
Three different and updated ML-based algorithms were developed to predict COVID-19 diagnosis; PLS-DA led to the most accurate results. High levels of some metabolites were found as potentially predictors of the disease. These biomarkers should be further evaluated as potential therapeutic targets in well-designed clinical trials. These ML-based models can help the early diagnosis of COVID-19 and guide the development of tailored interventions.
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.
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.
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.
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.
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.
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•63 compounds were identified in propolis extracts.•Propolis extracts exert selective synergistic cytotoxicity with doxorubicin.•Propolis extracts have antibacterial action.•Propolis extract exerts selective synergistic effect with gentamicin.
Although stingless bees are widespread in tropical and subtropical regions worldwide, their by-products, including propolis, are rarely used. In this study, we aimed to chemically analyze and ...investigate the potential of mid-infrared (MIR) spectroscopy and chemometric analyses for the authentication of propolis. The content of phenolic compounds and total flavonoids were used as quality parameters according to the guidelines of the Brazilian legislation. Attenuated total reflection-infrared (ATR-IR) spectroscopy was performed, and a chemometric model was developed and validated to discriminate and classify the four samples of propolis. The partial least squares-discriminant analysis (PLS-DA) model was found to be sensitive, specific, and accurate, and can be used for the quality control of these propolis samples for authentication purposes. Plebeia propolis showed the lowest total phenolic and flavonoid content. As suggested by the ATR-IR and confirmed by the determination of total phenolics and flavonoids, only green, tubuna, and mandaçaia propolis met the criteria established by the Brazilian legislation for marketing. In conclusion, the infrared chemometric model developed in this study can be implemented as a tool for the authentication of the studied propolis classes.
•Propolis from three species of stingless bees and green propolis were evaluated.•Content of phenolic compounds and total flavonoids were used as quality parameters.•A chemometric model was developed and validated using ATR-IR.•The PLS-DA model is sensitive, specific and accurate and can be used in quality control.•Mandaçaia and tubuna propolis extracts show potential to be used in industry.
To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.
Serum ...(n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.
The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.
The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients’ serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.
•Seven machine learning models were implemented and evaluated to predict the diagnosis and prognosis of COVID-19.•The PLS-DA model presented the best performance with accuracy values greater than 90%.•PLS-DA model can help predicting SARS-CoV-2 diagnosis, severity, and fatality in dailypractice.•New potential biomarkers for the diagnosis and prognosis of COVID-19 were identified.
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