Lithium metal is widely regarded as the "ultimate" anode for energy-dense Li batteries, but its high reactivity and delicate interface make it prone to dendrite formation, limiting its practical use. Inspired by self-assembled monolayers on metal surfaces, we propose a facile yet effective strategy to stabilize Li metal anodes by creating an artificial solid electrolyte interphase (SEI). Our method involves dip-coating Li metal in MPDMS to create an SEI layer that is rich in inorganic components, allowing uniform Li plating/stripping under a low overpotential over 500 cycles in carbonate electrolytes. In comparison, pristine Li metal shows a rapid increase in overpotential after merely 300 cycles, leading to failure soon after. Molecular dynamics simulations demonstrate that this uniform artificial SEI suppresses Li dendrite formation. We further demonstrated its enhanced stability pairing with LiFePO 4 and LiNi 1−x−y Co x Mn y O 2 cathodes, highlighting the proposed strategy as a promising solution for practical Li metal batteries.

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HIV/AIDS has the highest mortality among infectious diseases in China. In ongoing efforts to alleviate this crisis, the national government has placed great emphasis on efforts in Henan province where HIV-infected former plasma donors in the 1990s contributed to AIDS becoming a public health crisis. Concomitant with a national initiative focusing the use of phamacogenetics for the better prediction of treatment response, we studied genetic variants with known pharmacokinetic phenotypes in a set of 298 HAART-treated (highly active antiretroviral therapy) patients infected with HIV from the Henan cohort. We measured the association of response to treatment, assessed as changes in CD4+ T cell counts after antiretroviral therapy, of five polymorphisms in four genes (CYP2B6, ABCB1/MDR1, ABCG2, and ABCC4) in which variation has been suggested to affect the pharmacokinetics of drugs commonly employed to treat HIV/AIDS. We show that genotyping for ABCB1 variations (rs1045642 and rs2032582) may help predict HIV treatment response. We found variations in this gene have a significant association with outcome as measured by CD4+ T cell counts in a discovery subset (N = 197; odds ratio (OR) = 1.58; 95% CI 1.02–2.45), these results were confirmed in a validation subset of the cohort (N = 78; OR = 2.81; 95% CI 1.32–5.96). Exploratory analysis suggests that this effect may be specific to NVP (nevirapine) or 3TC (lamivudine) response. This publication represents the first genetic analysis in a continuing effort to study and assist the patients in a very large, unique, and historically significant HIV-AIDS cohort. Genotyping of AIDS patients for ABCB1 variation may help predict outcome and potentially could help guide treatment strategies.

CD4 count is the standard method for determining eligibility for highly active antiretroviral therapy (HAART) and monitoring HIV/AIDS disease progression, but it is not widely available in resource-limited settings. This study examined the correlation between total lymphocyte count (TLC) and CD4 count of HIV-infected patients before and after HAART, and assessed the thresholds of TLC for making decisions about the initiation and for monitoring HAART. A retrospective study was performed, and 665 HIV-infected patients with TLC and CD4 count from four counties (Shangcai, Queshan, Shenqiu and Weishi) were included in the study. Pearson correlation and receiver operating characteristic (ROC) were used. TLC and CD4 count after HAART was significantly increased as compared with pre-HAART (P<0.01). An overall positive correlation was noted between TLC and CD4 count (pre-HAART, r=0.73, P=0.0001; follow-up HAART, r=0.56, P=0.0001). The ROC curve between TLC and CD4 count showed that TLC ≤ 1200 cells/mm(3) could predict CD4 < 200 cells/mm(3) with a sensitivity of 71.12%, specificity of 66.35% at pre-HAART. After 12-month HAART, the optimum prediction for CD4 count < 200 cells/mm3 was a TLC ≤ 1300 cells/mm(3), with a sensitivity of 63.27%, and a specificity of 74.84%. Further finding indicated that TLC change was positively correlated to CD4 change (r=0.77, P=0.0001) at the time point of 12-month treatment, and the best prediction point of TLC change for CD4 increasing was 135 cells/mm(3). TLC and its change can be used as a surrogate marker for CD4 count and its change of HIV-infected individuals for making decisions about the initiation and for monitoring HAART in resource-limited settings.