Nasal spray of an IgM-like ACE2 fusion protein HH-120 accelerates SARS-CoV-2 clearance: A single-center propensity score-matched cohort study.

HH-120, a recently developed IgM-like ACE2 fusion protein with broad-spectrum neutralizing activity against all ACE2-utilizing coronaviruses, has been developed as a nasal spray for use as an early treatment agent to reduce disease progression and airborne transmission. The objective of this study was to evaluate the safety and efficacy of the HH-120 nasal spray in SARS-CoV-2-infected subjects. Eligible symptomatic or asymptomatic SARS-CoV-2-infected participants were enrolled in a single-arm trial to receive the HH-120 nasal spray for no longer than 6 days or until viral clearance at a single hospital between August 3 and October 7, 2022. An external control was built from real-world data of SARS-CoV-2-infected subjects contemporaneously hospitalized in the same hospital using a propensity score matching (PSM) method. After PSM, 65 participants in the HH-120 group and 103 subjects with comparable baseline characteristics in the external control group were identified. The viral clearance time was significantly shorter in participants receiving the HH-120 nasal spray than that in subjects of the control group (median 8 days vs. 10 days, p < 0.001); the difference was more prominent in those subgroup subjects with higher baseline viral load (median 7.5 days vs. 10.5 days, p < 0.001). The incidence of treatment-emergent adverse events and treatment-related adverse events of HH-120 group were 35.1% (27/77) and 3.9% (3/77), respectively. All the adverse events observed were mild, being of CTCAE grade 1 or 2, and transient. The HH-120 nasal spray showed a favorable safety profile and promising antiviral efficacy in SARS-CoV-2-infected subjects. The results from this study warrant further assessment of the efficacy and safety of the HH-120 nasal spray in large-scale randomized controlled clinical trials.

Effect of warning signs on the epidemic spreading of the COVID-19 pandemic

Early warning signs of the outbreak of pandemic disease become a high profile from the beginning and they remind more susceptible individuals to keep social distance on social occasions. However, these signs have no way to the Susceptible–Infected–Recovered (SIR) models which have been concerned by medical scientists. Warning signs imply the risk level of the pandemic disease evaluated by the government. The response of susceptible population (S-population) to the warning signs is represented by a chicken game. In order to get a better payoff, the more beneficial behavior of the S-population may be induced in the autonomous society based on the SIR model. We emphasize that participants can choose their strategies whether to follow the health rules or not without coercion in the chicken game while the warning signs released by the policy makers can encourage S-population to choose beneficial behavior, instead of purely following the healthy rules or not. The agile policy helps S-population to make a choice on the basis of risk interests but without losing to protect themselves in a serious pandemic situation. Comparing the classic SIR model with our signal-SIR model, the serious pandemic signal released by the policy makers and the disease awareness to it together play an important role in the outbreak period of the pandemic disease. [ FROM AUTHOR]

Scorpion-Shaped Zinc Porphyrins as Tetrafunctional TAR RNA Predators and HIV-1 Reverse Transcriptase Inhibitors.

HIV-1 reverse transcriptase (RT) inhibitors are fundamental to the discovery and development of anti-HIV drugs. Their main target is RT, and only a tiny number of them can bind to viral RNA. In this paper, five new Zn(II) porphyrin compounds were developed with different characters. ZnTPP4 has both the appearance and the functions of a scorpion with a rigid tail and stinger to selectively hunt HIV-1 TAR RNA based on the molecular recognition of hydrogen bonds, a fierce chelicera to bite RNA by metal coordination, mighty pedipalps to grasp the bound RNA by supramolecular inclusion, and a broad body maintaining the configuration of each functional area so that they can cooperate with each other and providing accommodation space for the bound RNA. This tetrafunctional Zn(II) porphyrin is relatively nontoxic to normal cells and can produce sensitive responses for RNA. Moreover, this work offers practical construction methodologies for medication of AIDS and other diseases closely related to RT like EBOV and SARS-CoV-2.

Polypyridyl ruthenium complexes as bifunctional TAR RNA binders and HIV-1 reverse transcriptase inhibitors.

Inhibitors of type 1 human immunodeficiency virus (HIV-1) reverse transcriptase are central to anti-HIV therapy. Most of their targets are enzymes, while very few could bind to viral RNA. Here we designed four new polypyridyl Ru(II) complexes, which could bind HIV-1 TAR RNA tightly and selectively by molecular recognition of hydrogen bonds, further stabilize the Ru(II)-RNA bound system by electrostatic attraction, and efficiently inhibit the Moloney murine leukemia virus (M-MuLV) and HIV-1 reverse transcriptase. The polypyridyl Ru(II) complexes also have physical and chemical advantages, including high chemical stability and photostability, sensitive spectroscopic responses to HIV TAR RNA, and low toxicity to normal cells. This work also provides valuable drug design strategies for acquired immune deficiency syndrome (AIDS) and other reverse transcriptase related disease research, such as hepatitis C virus (HCV), Ebola virus (EBOV), influenza A virus, and most recently the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

DCML: Deep contrastive mutual learning for COVID-19 recognition.

COVID-19 is a form of disease triggered by a new strain of coronavirus. Automatic COVID-19 recognition using computer-aided methods is beneficial for speeding up diagnosis efficiency. Current researches usually focus on a deeper or wider neural network for COVID-19 recognition. And the implicit contrastive relationship between different samples has not been fully explored. To address these problems, we propose a novel model, called deep contrastive mutual learning (DCML), to diagnose COVID-19 more effectively. A multi-way data augmentation strategy based on Fast AutoAugment (FAA) was employed to enrich the original training dataset, which helps reduce the risk of overfitting. Then, we incorporated the popular contrastive learning idea into the conventional deep mutual learning (DML) framework to mine the relationship between diverse samples and created more discriminative image features through a new adaptive model fusion method. Experimental results on three public datasets demonstrate that the DCML model outperforms other state-of-the-art baselines. More importantly, DCML is easier to reproduce and relatively efficient, strengthening its high practicality.