| DB ID | MyCo_5028 |
| Title | Machine learning based predictive model and systems-level network of host-microbe interactions in post-COVID-19 mucormycosis |
| Year | 2021 |
| PMID | 34861346 |
| Fungal Diseases involved | Mucormycosis |
| Associated Medical Condition | COVID-19 |
| Genus | Rhizopus |
| Species | arrhizus |
| Organism | Rhizopus arrhizus |
| Ethical Statement | None |
| Site of Infection | None |
| Opportunistic invasive | Opportunistic |
| Sample type | None |
| Sample source | None |
| Host Group | Human |
| Host Common name | Human |
| Host Scientific name | Homo sapiens |
| Biomarker Name | RPS6 |
| Biomarker Full Name | Ribosomal Protein S6 |
| Biomarker Type | Diagnostic |
| Biomolecule | Protein |
| Geographical Location | India |
| Cohort | None |
| Cohort No. | None |
| Age Group | None |
| P Value | None |
| Sensitivity | None |
| Specificity | None |
| Positive Predictive Value | None |
| MIC | None |
| Fold Change | None |
| Pathway | None |
| Disease Introduction Mechanism | Mucormycosis is a sporadic fatal fungal infection caused by Mucorales such as Rhizopus, Rhizomucor, Mucor, Cunninghamella and Absidia. The prevalence of mucormycosis is ~0.14 cases per 1000 in India. The identification of mucormycosis in individuals with history of Coronavirus disease (COVID-19) is a rising problem of distress especially after second wave. COVID-19 presents with varying symptomatic patterns, ranging from mild to moderate to lethal such as immune modulation, dizziness, mood changes, weight gain, insomnia, muscle weakness, diabetes mellitus and secondary infections. |
| Technique | Machine learning Approach |
| Analysis Method | Machine learning Approach |
| ELISA kits | None |
| Assay Data | None |
| Validation Techniques used | Machine learning Approach |
| Up Regulation Down Regulation | Positive |
| Sequence Data | None |
| External Link | None |
