| Autor: |
Cheng P; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China.; School of Public Health, Kunming Medical University, Kunming, China., He BC; School of Public Health, Kunming Medical University, Kunming, China., Wu ZX; School of Public Health, Kunming Medical University, Kunming, China., Liu JF; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China., Wang JL; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China., Yang CX; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China., Ma S; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China., Zhang M; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China., Dong XQ; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China.; School of Public Health, Kunming Medical University, Kunming, China., Li JJ; Department of Laboratory Medicine, Yunnan Provincial Hospital of Infectious Diseases, Kunming, China. |
| Abstrakt: |
Heterosexuals have become the most prevalent group of HIV-1 in Kunming, Yunnan Province. Utilizing the principle of genetic similarity between their gene sequences, we built a molecular transmission network by gathering data from earlier molecular epidemiological studies. This allowed us to analyze the epidemiological features of this group and offer fresh concepts and approaches for the prevention and management of HIV-1 epidemics. Cytoscope was used to visualize and characterize the network following the processing of the sample gene sequences by BioEdit and HyPhy. The number of possible links and the size of the clusters were investigated as influencing factors using a zero-inflated Poisson model and a logistic regression model, respectively. A scikit-learn-based prediction model was developed to account for the dynamic changes in the HIV-1 molecular network. Six noteworthy modular clusters with network scores ranging from 4 to 9 were found from 150 clusters using Molecular Complex Detection analysis at a standard genetic distance threshold of 0.01. The size of the number of possible links and the network's clustering rate were significantly impacted by sampling time, marital status, and CD4 + T lymphocytes (all p < 0.05). The gradient boosting machine (GBM) model had the highest area under the curve value, 0.884 ± 0.051, according to scikit-learn. Though not all cluster subtypes grew equally, the network clusters were relatively specific and aggregated. The largest local transmission-risk group for HIV-1CRF08_BC is now the heterosexual transmission population. The most suitable model for constructing the HIV-1 molecular network dynamics prediction model was found to be the GBM model. |
