COVID-19 disease causes an energy supply deficit in a patient

Comparative analysis of oropharyngeal microbiota in healthcare workers post-COVID-19

Background

To date, more than 770 million individuals have become coronavirus disease 2019 (COVID-19) convalescents worldwide. Emerging evidence highlights the influence of COVID-19 on the oral microbiome during both acute and convalescent disease phases. Front-line healthcare workers are at an elevated risk of exposure to viral infections, and the effects of COVID-19 on their oral microbiome remain relatively unexplored.

Methods

Oropharyngeal swab specimens, collected one month after a negative COVID-19 test from a cohort comprising 55 healthcare workers, underwent 16S rRNA sequencing. We conducted a comparative analysis between this post-COVID-19 cohort and the pre-infection dataset from the same participants. Community composition analysis, indicator species analysis, alpha diversity assessment, beta diversity exploration, and functional prediction were evaluated.

Results

The Shannon and Simpson indexes of the oral microbial community declined significantly in the post-COVID-19 group when compared with the pre-infection cohort. Moreover, there was clear intergroup clustering between the two groups. In the post-COVID-19 group, the phylum Firmicutes showed a significant increase. Further, there were clear differences in relative abundance of several bacterial genera in contrast with the pre-infection group, including Streptococcus, Gemella, Granulicatella, Capnocytophaga, Leptotrichia, Fusobacterium, and Prevotella. We identified Gemella enrichment in the post-COVID-19 group, potentially serving as a recovery period performance indicator. Functional prediction revealed lipopolysaccharide biosynthesis downregulation in the post-COVID-19 group, an outcome with host inflammatory response modulation and innate defence mechanism implications.

Conclusion

During the recovery phase of COVID-19, the oral microbiome diversity of front-line healthcare workers failed to fully return to its pre-infection state. Despite the negative COVID-19 test result one month later, notable disparities persisted in the composition and functional attributes of the oral microbiota.

SARS-CoV-2 strain wars continues: Chemical and thermodynamic characterization of live matter and biosynthesis of Omicron BN.1, CH.1.1 and XBC variants

SARS-CoV-2 has during the last 3 years mutated several dozen times. Most mutations in the newly formed variants have been chemically and thermodynamically characterized. New variants have been declared as variants under monitoring. The European Centre for Disease Prevention and Control has suggested the hypothesis that the new BN.1, CH.1.1 and XBC variants could have properties similar to those of VOC. Thermodynamic properties of new variants have been reported in this manuscript for the first time. Gibbs energy of biosynthesis, as the driving force for viral multiplication, is less negative for the new variants than for the earlier variants. This indicates that the virus has evolved towards decrease in pathogenicity, which leads to less severe forms of COVID-19.

XBB.1.5 Kraken cracked: Gibbs energies of binding and biosynthesis of the XBB.1.5 variant of SARS-CoV-2

The SARS-CoV-2 Hydra with many heads (variants) has been causing the COVID-19 pandemic for 3 years. The appearance of every new head (SARS-CoV-2 variant) causes a new pandemic wave. The last in the series is the XBB.1.5 "Kraken" variant. In the general public (social media) and in the scientific community (scientific journals), during the last several weeks since the variant has appeared, the question was raised of whether the infectivity of the new variant will be greater. This article attempts to provide the answer. Analysis of thermodynamic driving forces of binding and biosynthesis leads to the conclusion that infectivity of the XBB.1.5 variant could be increased to a certain extent. The pathogenicity of the XBB.1.5 variant seems to be unchanged compared to the other Omicron variants.

The SARS-CoV-2 Hydra, a tiny monster from the 21st century: Thermodynamics of the BA.5.2 and BF.7 variants

SARS-CoV-2 resembles the ancient mythical creature Hydra. Just like with the Hydra, when one head is cut, it is followed by appearance of two more heads, suppression of one SARS-CoV-2 variant causes appearance of newer variants. Unlike Hydra that grows identical heads, newer SARS-CoV-2 variants are usually more infective, which can be observed as time evolution of the virus at hand, which occurs through acquisition of mutations during time. The appearance of new variants is followed by appearance of new COVID-19 pandemic waves. With the appearance of new pandemic waves and determining of sequences, in the scientific community and general public the question is always raised of whether the new variant will be more virulent and more pathogenic. The two variants characterized in this paper, BA.5.2 and BF.7, have caused a pandemic wave during the late 2022. This paper gives full chemical and thermodynamic characterization of the BA.5.2 and BF.7 variants of SARS-CoV-2. Having in mind that Gibbs energy of binding and biosynthesis represent the driving forces for the viral life cycle, based on the calculated thermodynamic properties we can conclude that the newer variants are more infective than earlier ones, but that their pathogenicity has not changed.

Never ending story? Evolution of SARS-CoV-2 monitored through Gibbs energies of biosynthesis and antigen-receptor binding of Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants

RNA viruses exhibit a great tendency to mutate. Mutations occur in the parts of the genome that encode the spike glycoprotein and less often in the rest of the genome. This is why Gibbs energy of binding changes more than that of biosynthesis. Starting from 2019, the wild type that was labeled Hu-1 has during the last 3 years evolved to produce several dozen new variants, as a consequence of mutations. Mutations cause changes in empirical formulas of new virus strains, which lead to change in thermodynamic properties of biosynthesis and binding. These changes cause changes in the rate of reactions of binding of virus antigen to the host cell receptor and the rate of virus multiplication in the host cell. Changes in thermodynamic and kinetic parameters lead to changes in biological parameters of infectivity and pathogenicity. Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has been evolving towards increase in infectivity and maintaining constant pathogenicity, or for some variants a slight decrease in pathogenicity. In the case of Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants pathogenicity is identical as in the Omicron BA.2.75 variant. On the other hand, infectivity of the Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants is greater than those of previous variants. This will most likely result in the phenomenon of asymmetric coinfection, that is circulation of several variants in the population, some being dominant.

Lighting up ATP in cells and tissues using a simple aptamer-based fluorescent probe

Extracellular ATP as a purinergic signaling molecule, together with ATP receptor, are playing an important role in tumor growth, therapy resistance, and host immunity suppression. Meanwhile ATP is a crucial indicator for cellular energy status and viability, thus a vital variable for tissue regeneration and in vitro tissue engineering. Most recent studies on COVID-19 virus suggest infection caused ATP deficit and release as a major characterization at the early stage of the disease and major causes for disease complications. Thus, imaging ATP molecule in both cellular and extracellular contexts has many applications in biology, engineering, and clinics. A sensitive and selective fluorescence “signal-on” probe for ATP detection was constructed, based on the base recognition between a black hole quencher (BHQ)-labeled aptamer oligonucleotide and a fluorophore (Cy5)-labeled reporter flare. The probe was able to detect ATP in solution with single digit µM detection limit. With the assistance of lipofectamine, this probe efficiently entered and shined in the model cells U2OS within 3 h. Further application of the probe in specific scenery, cardio-tissue engineering, was also tested where the ATP aptamer complex was able to sense cellular ATP status in a semi-quantitative manner, representing a novel approach for selection of functional cardiomyocytes for tissue engineering. At last a slight change in probe configuration in which a flexible intermolecular A14 linker was introduced granted regeneration capability. These data support the application of this probe in multiple circumstances where ATP measurement or imaging is on demand.

Classification of the social distance during the COVID-19 pandemic from electricity consumption using artificial intelligence

Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.