The microbiota-related coinfections in COVID-19 patients: a real challenge

COVID-19 on Oral Health: A New Bilateral Connection for the Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and transmission are generally known to be produced by respiratory droplets and aerosols from the oral cavity (O.C.) of infected subjects, as stated by the World Health Organization. Saliva also retains the viral particles and aids in the spread of COVID-19. Angiotensin-converting enzyme Type 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) are two of the numerous factors that promote SARS-CoV-2 infection, expressed by O.C. structures, various mucosa types, and the epithelia of salivary glands. A systemic SARS-CoV-2 infection might result from viral replication in O.C. cells. On the other hand, cellular damage of different subtypes in the O.C. might be associated with various clinical signs and symptoms. Factors interfering with SARS-CoV-2 infection potential might represent fertile ground for possible local pharmacotherapeutic interventions, which may confine SARS-CoV-2 virus entry and transmission in the O.C., finally representing a way to reduce COVID-19 incidence and severity.

Impact of COVID-19 on the distribution of pathogenic bacteria in the lower respiratory tract of the elderly

BACKGROUND

To investigate the distribution of bacterial pathogens of lower respiratory tract infection (LRTI) in hospitalized elderly patients during the COVID-19 epidemic and to explore the influence of COVID-19 on the distribution of bacterial pathogens, to provide guidance for clinical diagnosis.

METHODS

Specimens of sputum from elderly LRTIs patients at Fuding Hospital of China were consecutively collected from October 2022 to January 2023. Cultures and identification were done, and RT-PCR was employed to detect SARS-Cov-2 nucleic acid.

RESULTS

A total of 195 isolates were characterized in 163 sputum samples of consecutive hospitalized elderly patients, of which 11.3% were Gram-positive bacteria and 88.7% were Gram-negative. The top of frequently isolated pathogens was Klebsiella pneumonia (30.3%), Pseudomonas aeruginosa (19.0%), Acinetobacter baumannii (12.8%), Stenotrophomonas maltophili, (7.7%), Escherichia coli (7.2%). According to the results of novel coronavirus nucleic acid detection, the 163 patients were divided into COVID-19 group and non-COVID control (CNT) group. The comparison of bacterial distribution between the groups revealed that Stenotrophomonas maltophilia was lower in the COVID-19 than in the CNT group, while A. baumannii was higher in the COVID-19 group, and the difference was statistically significant (p < .05).

CONCLUSION

The major bacteria identified in sputum culture of hospitalized elderly patients were K. pneumonia, P. aeruginosa, A. baumannii, S. maltophilia, and E. coli. Furthermore, the distribution of S. maltophilia and A. baumannii between the COVID-19 and CNT groups was found to be significantly different (p < .05), while there were no significant differences in the distribution of other bacteria.

Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach

During the second phase of SARS-CoV-2, an unknown fungal infection, identified as black fungus, was transmitted to numerous people among the hospitalized COVID-19 patients and increased the death rate. The black fungus is associated with the Mycolicibacterium smegmatis, Mucor lusitanicus, and Rhizomucor miehei microorganisms. At the same time, other pathogenic diseases, such as the Monkeypox virus and Marburg virus, impacted global health. Policymakers are concerned about these pathogens due to their severe pathogenic capabilities and rapid spread. However, no standard therapies are available to manage and treat those conditions. Since the coptisine has significant antimicrobial, antiviral, and antifungal properties; therefore, the current investigation has been designed by modifying coptisine to identify an effective drug molecule against Black fungus, Monkeypox, and Marburg virus. After designing the derivatives of coptisine, they have been optimized to get a stable molecular structure. These ligands were then subjected to molecular docking study against two vital proteins obtained from black fungal pathogens: Rhizomucor miehei (PDB ID: 4WTP) and Mycolicibacterium smegmatis (PDB ID 7D6X), and proteins found in Monkeypox virus (PDB ID: 4QWO) and Marburg virus (PDB ID 4OR8). Following molecular docking, other computational investigations, such as ADMET, QSAR, drug-likeness, quantum calculation and molecular dynamics, were also performed to determine their potentiality as antifungal and antiviral inhibitors. The docking score reported that they have strong affinities against Black fungus, Monkeypox virus, and Marburg virus. Then, the molecular dynamic simulation was conducted to determine their stability and durability in the physiological system with water at 100 ns, which documented that the mentioned drugs were stable over the simulated time. Thus, our in silico investigation provides a preliminary report that coptisine derivatives are safe and potentially effective against Black fungus, Monkeypox virus, and Marburg virus. Hence, coptisine derivatives may be a prospective candidate for developing drugs against Black fungus, Monkeypox and Marburg viruses.

Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach

Objective

Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis.

Methods

Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy.

Results

From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis.

Conclusion

The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.

SARS‐CoV‐2 and its variants of concern including Omicron: looks like a never ending pandemic

The ongoing COVID‐19 pandemic caused by SARS‐CoV‐2 is associated with high morbidity and mortality. This zoonotic virus has emerged in Wuhan of China in December 2019 from bats and pangolins probably and continuing the human‐to‐human transmission globally since last two years. As there is no efficient approved treatment, a number of vaccines were developed at an unprecedented speed to counter the pandemic. Moreover, vaccine hesitancy is observed that may be another possible reason for this never ending pandemic. In the meantime, several variants and mutations were identified and causing multiple waves globally. Now the safety and efficacy of these vaccines are debatable and recommended to determine whether vaccines are able to interrupt transmission of SARS‐CoV‐2 variant of concern (VOC). Moreover, the VOCs continue to emerge that appear more transmissible and less sensitive to virus‐specific immune responses. In this overview, we have highlighted various drugs and vaccines used to counter this pandemic along with their reported side effects. Moreover, the preliminary data for the novel VOC “Omicron” are discussed with the existing animal models.

Does the Fetus Limit Antibiotic Treatment in Pregnant Patients with COVID-19?

During pregnancy, there is a state of immune tolerance that predisposes them to viral infection, causing maternal-fetal vulnerability to the adverse effects of COVID-19. Bacterial coinfections significantly increase the mortality rate for COVID-19. However, it is known that all drugs, including antibiotics, will enter the fetal circulation in a variable degree despite the role of the placenta as a protective barrier and can cause teratogenesis or other malformations depending on the timing of exposure to the drug. Also, it is important to consider the impact of the indiscriminate use of antibiotics during pregnancy can alter both the maternal and fetal-neonatal microbiota, generating future repercussions in both. In the present study, the literature for treating bacterial coinfections in pregnant women with COVID-19 is reviewed. In turn, we present the findings in 50 pregnant women hospitalized diagnosed with SARS-CoV-2 without previous treatment with antibiotics; moreover, a bacteriological culture of sample types was performed. Seven pregnant women had coinfection with Staphylococcus haemolyticus, Staphylococcus epidermidis, Streptococcus agalactiae, Escherichia coli ESBL +, biotype 1 and 2, Acinetobacter jahnsonii, Enterococcus faecium, and Clostridium difficile. When performing the antibiogram, resistance to multiple drugs was found, such as macrolides, aminoglycosides, sulfa, dihydrofolate reductase inhibitors, beta-lactams, etc. The purpose of this study was to generate more scientific evidence on the better use of antibiotics in these patients. Because of this, it is important to perform an antibiogram to prevent abuse of empirical antibiotic treatment with antibiotics in pregnant women diagnosed with SARS-CoV-2.