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To comprehensively investigate the molecular transmission patterns of HIV-1 genotypes among men who have sex with men (MSM) in Chongqing, we employed 392 pol sequences of MSM to construct a phylogenetic tree and gene transmission network. Among the viral subtypes, CRF07_BC accounted for 73.2% (287/392) and CRF01_AE accounted for 20.7% (81/392), emerging as the predominant subtypes in this investigation. Additionally, we observed the presence of CRF55_01B, subtype B, CRF08_BC and other circulating recombinant forms. The HIV-1 molecular network was constructed with a gene distance threshold of 1.5%, resulting in an entry rate of 61.4% (241/392). Within the network, we identified a total of 23 molecular clusters, with the largest cluster being the CRF07_BC molecular cluster comprising 148 node values. Transmitted drug-resistance (TDR) mutations were found in 4.34% of the cases, with 1.79% associated with protease inhibitors (PIs), 0.51% with nucleoside reverse transcriptase inhibitors (NRTIs), and 2.55% with non-nucleoside reverse transcriptase inhibitors (NNRTIs). Statistical analysis indicated a higher enrollment rate in the HIV-1 molecular network among infected individuals with the CRF07_BC subtype, those identifying with same-sex sexual roles as “vers,” and individuals with higher education levels. This suggests the need for strengthened investigation and intervention in this population to prevent the formation of larger transmission clusters. Furthermore, continuous monitoring of the HIV-1 molecular dynamics network is necessary to promptly and accurately track changes in molecular epidemic characteristics.

Abstract Background Over the past few years, HIV transmission among men who have sex with men (MSM) in China has increased significantly. Chongqing, located in the southwest of China, has the highest prevalence of HIV among MSM in the country. Methods Blood samples were taken from 894 MSM in Chongqing who had recently been diagnosed with HIV-1 infection and had not yet started getting treatment. In order to determine the distribution of HIV-1 subtypes, transmitted drug resistance, and assessments of molecularly transmitted clusters, we sequenced the Pol genes and employed them in phylogenetic analysis. The genetic distance between molecular clusters was 1.5%. To find potential contributing factors, logistic regression analyses were performed. Results Of the 894 HIV-1 pol sequences acquired from study participants, we discovered that CRF07_BC (73.6%) and CRF01_AE (19.6%) were the two most prevalent HIV-1 genotypes in Chongqing among MSM, accounting for 93.2% of all infections. In addition, CRF08_BC (1.1%), B subtype (1.0%), CRF55_01B (3.4%), and URF/Other subtypes (1.3%) were less frequently observed. Among MSM in Chongqing, transmitted drug resistance (TDR) was reported to be present at a rate of 5.6%. 48 clusters with 600 (67.1%, 600/894) sequences were found by analysis of the molecular transmission network. The distributions of people by age, sexual orientation, syphilis, and genotype were significantly differentially related to being in clusters, according to the multivariable logistic regression model. Conclusion Despite the low overall prevalence of TDR, the significance of genotypic drug resistance monitoring needs to be emphasized. CRF07_BC and CRF01_AE were the two main genotypes that created intricate molecular transmission networks. In order to prevent the expansion of molecular networks and stop the virus’s spread among MSM in Chongqing, more effective HIV intervention plans should be introduced.

We consider the problem of predicting the impression that one subject has of another in a video clip showing a group of interacting people. Our novel Dyadic Impression Prediction System (DIPS) contains two major innovations. First, we develop a novel method to align the facial expressions of subjects pi and pj as well as account for the temporal delay that might be involved in pi reacting to pj 's facial expressions. Second, we propose the concept of a multilayered stochastic network for impression prediction on top of which we build a novel Temporal Delayed Network graph neural network architecture. Our overall DIPS architecture predicts six dependent variables relating to the impression pi has of pj . Our experiments show that DIPS beats eight baselines from the literature, yielding statistically significant improvements of 19.9% to 30.8% in AUC and 12.6% to 47.2% in F1-score. We further conduct ablation studies showing that our novel features contribute to the overall quality of the predictions made by DIPS. [ABSTRACT FROM AUTHOR]