The ability of donor cells to engraft without evidence of ongoing HIV-1 infection suggests that HIV-1 replication may be fully suppressed during cART and does not contribute to maintenance of viral reservoirs in peripheral blood in our patients. HSCTs with wild-type-CCR5(+) donor cells can lead to a sustained reduction in the size of the peripheral reservoir of HIV-1.

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BACKGROUNDAn alternative to epidemiological models for transmission dynamics of Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time forecasting of Covid-19 to estimate the size, lengths and ending time of Covid-19 across China. METHODSWe developed a modified stacked auto-encoder for modeling the transmission dynamics of the epidemics. We applied this model to real-time forecasting the confirmed cases of Covid-19 across China. The data were collected from January 11 to February 27, 2020 by WHO. We used the latent variables in the auto-encoder and clustering algorithms to group the provinces/cities for investigating the transmission structure. RESULTSWe forecasted curves of cumulative confirmed cases of Covid-19 across China from Jan 20, 2020 to April 20, 2020. Using the multiple-step forecasting, the estimated average errors of 6step, 7-step, 8-step, 9-step and 10-step forecasting were 1.64%, 2.27%, 2.14%, 2.08%, 0.73%, respectively. We predicted that the time points of the provinces/cities entering the plateau of the forecasted transmission dynamic curves varied, ranging from Jan 21 to April 19, 2020. The 34 provinces/cities were grouped into 9 clusters. CONCLUSIONSThe accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. We predicted that the epidemics of Covid-19 will be over by the middle of April. If the data are reliable 3 and there are no second transmissions, we can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China. The AI-inspired methods are a powerful tool for helping public health planning and policymaking.

HIV posttreatment controllers (PTCs) represent a natural model of sustained HIV remission, but they are rare and little is known about their viral reservoir. We obtained 1,450 proviral sequences after near-full-length amplification for 10 PTCs and 16 posttreatment noncontrollers (NCs). Before treatment interruption, the median intact and total reservoir size in PTCs was 7-fold lower than in NCs, but the proportion of intact, defective, and total clonally expanded proviral genomes was not significantly different between the 2 groups. Quantification of total but not intact proviral genome copies predicted sustained HIV remission as 81% of NCs, but none of the PTCs had a total proviral genome greater than 4 copies per million peripheral blood mononuclear cells (PBMCs). The results highlight the restricted intact and defective HIV reservoir in PTCs and suggest that total proviral genome burden could act as the first biomarker for identifying PTCs. Total and defective but not intact proviral copy numbers correlated with levels of cell-associated HIV RNA, activated NK cell percentages, and both HIV-specific CD4+ and CD8+ responses. These results support the concept that defective HIV genomes can lead to viral antigen production and interact with both the innate and adaptive immune systems.