A True Non-Newtonian Electrolyte for Rechargeable Hybrid Aqueous Battery

The rechargeable aqueous hybrid battery is a unique system in which the Li-ion mechanism dominates the cathode while the first-order metal reaction of stripping/depositing regulates the anode. This battery inherits the advantages of the low-cost anode while possessing the capability of the Li-ion cathode. One of the major challenges is to design a proper electrolyte to nourish such strengths and alleviate the downsides, because two different mechanisms are functioning separately at the node–electrolyte and the cathode–electrolyte interfaces. In this work, we design a non-Newtonian electrolyte which offers many advantages for a Zn/LiMn2O4 battery. The corrosion is kept low while almost non-dendritic zinc deposition is confirmed by chronoamperometry and ex situ microscopy. The gel strength and gelling duration of such non-Newtonian electrolytes can be controlled. The ionic conductivity of such gels can reach 60 mS⋅cm−1. The battery exhibits reduced self-discharge, 6–10% higher specific discharge capacity than the aqueous reference battery, high rate capability, nearly 80% capacity retention after 1000 cycles, and about 100 mAh⋅g−1 of specific discharge capacity at cycle No. 1000th. Negligible amorphization on the cathode surface and no passivation on the anode surface are observed after 1000 cycles, evidenced by X-ray diffraction and scanning electron microscopy on the post-run battery electrodes

Innovative Virtual Screening of PD-L1 Inhibitors: The Synergy of Molecular Similarity, Neural Networks, and GNINA Docking

Immune checkpoint inhibitors have garnered significant attention in oncological research over recent years. A plethora of studies have elucidated that inhibitors targeting the Programmed Death-Ligand 1 (PD-L1) play a pivotal role in circumventing the evasion mechanisms of cancer cells against the immune system. This study aimed to develop an integrated screening model combining an Artificial Neural Network (ANN), Molecular Similarity (MS) assessments, and GNINA 1.0 molecular docking, targeting PD-L1 inhibitors. A database of 2044 substances with known PD-L1 inhibitory activity was compiled from Google Patents and used to enhance molecular similarity evaluations and train the machine learning model. For retrospective validation of the docking procedure, the human PD-L1 protein, with the Protein Data Bank (PDB) ID: 5N2F, was employed as a control. In this phase of the study, 15,235 compounds from the DrugBank database were subjected to a series of screening processes: initially through medicinal chemistry filters, followed by MS assessments, the ANN model, and culminating with molecular docking using GNINA 1.0. The decoy generation yielded promising outcomes, evidenced by an AUC-ROC 1NN value of 0.52 and Doppelganger scores with a mean of 0.24 and a maximum of 0.346, indicating a high resemblance of the decoys to the active set. For MS, the AVALON emerged as the most effective fingerprint for similarity searching, demonstrating an Enrichment Factor (EF) of 1% at 10.96%, an AUC-ROC of 0.963, and an optimal similarity threshold of 0.32. The ANN model demonstrated superior performance in cross-validation, achieving an average precision of 0.863±0.032 and an F1 score of 0.745±0.039, outperforming both the Support Vector Classifier (SVC) and Random Forest (RF) models, albeit not significantly. In external validation, the ANN model maintained its superiority with an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0, employed for molecular docking, was validated through redocking and retrospective control, achieving an AUC of 0.975, with a critical cnn_pose_score threshold of 0.73. From the initial 15,235 compounds, 128 were shortlisted using the MS and ANN models. Further screening through GNINA 1.0 identified 22 potential candidates, among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising, with a cnn_pose_score of 0.79, a PD-L1 inhibitory probability of 70.5%, and a Tanimoto coefficient of 0.35

Timing of initiation of antiretroviral therapy in human immunodeficiency virus (HIV)--associated tuberculous meningitis

The optimal time to initiate antiretroviral therapy (ART) in human immunodeficiency virus (HIV)-associated tuberculous meningitis is unknown. We conducted a randomized, double-blind, placebo-controlled trial of immediate versus deferred ART in patients with HIV-associated tuberculous meningitis to determine whether immediate ART reduced the risk of death. Antiretroviral drugs (zidovudine, lamivudine, and efavirenz) were started either at study entry or 2 months after randomization. All patients were treated with standard antituberculosis treatment, adjunctive dexamethasone, and prophylactic co-trimoxazole and were followed up for 12 months. We conducted intention-to-treat, per-protocol, and prespecified subgroup analyses. A total of 253 patients were randomized, 127 in the immediate ART group and 126 in the deferred ART group; 76 and 70 patients died within 9 months in the immediate and deferred ART groups, respectively. Immediate ART was not significantly associated with 9-month mortality (hazard ratio [HR], 1.12; 95% confidence interval [CI], .81-1.55; P = .50) or the time to new AIDS events or death (HR, 1.16; 95% CI, .87-1.55; P = .31). The percentage of patients with severe (grade 3 or 4) adverse events was high in both arms (90% in the immediate ART group and 89% in the deferred ART group; P = .84), but there were significantly more grade 4 adverse events in the immediate ART arm (102 in the immediate ART group vs 87 in the deferred ART group; P = .04). Immediate ART initiation does not improve outcome in patients presenting with HIV-associated tuberculous meningitis. There were significantly more grade 4 adverse events in the immediate ART arm, supporting delayed initiation of ART in HIV-associated tuberculous meningitis. Clinical Trials Registration. ISRCTN6365909